COVID_Start_Jupyter_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bf61f06",
   "metadata": {},
   "source": [
    "# COVID-Risk-Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c54f17",
   "metadata": {},
   "source": [
    "## Data Import and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0dfb812c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>207548.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>211728.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>213258.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151776 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan  2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe  2021-12-29     207548.0        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe  2021-12-30     211728.0     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe  2021-12-31     213258.0     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe  2022-01-01     214214.0      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe  2022-01-02     214214.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN           NaN         NaN                  NaN   \n",
       "1                      NaN           NaN         NaN                  NaN   \n",
       "2                      NaN           NaN         NaN                  NaN   \n",
       "3                      NaN           NaN         NaN                  NaN   \n",
       "4                      NaN           NaN         NaN                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "151771            1163.429        4940.0         0.0               16.000   \n",
       "151772            1483.429        4997.0        57.0               20.286   \n",
       "151773            1503.143        5004.0         7.0               19.000   \n",
       "151774            1495.429        5017.0        13.0               18.857   \n",
       "151775                 NaN        5017.0         NaN                  NaN   \n",
       "\n",
       "        ...  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0       ...             NaN           NaN                  37.746   \n",
       "1       ...             NaN           NaN                  37.746   \n",
       "2       ...             NaN           NaN                  37.746   \n",
       "3       ...             NaN           NaN                  37.746   \n",
       "4       ...             NaN           NaN                  37.746   \n",
       "...     ...             ...           ...                     ...   \n",
       "151771  ...             1.6          30.7                  36.791   \n",
       "151772  ...             1.6          30.7                  36.791   \n",
       "151773  ...             1.6          30.7                  36.791   \n",
       "151774  ...             1.6          30.7                  36.791   \n",
       "151775  ...             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                              0.5            64.83                    0.511   \n",
       "1                              0.5            64.83                    0.511   \n",
       "2                              0.5            64.83                    0.511   \n",
       "3                              0.5            64.83                    0.511   \n",
       "4                              0.5            64.83                    0.511   \n",
       "...                            ...              ...                      ...   \n",
       "151771                         1.7            61.49                    0.571   \n",
       "151772                         1.7            61.49                    0.571   \n",
       "151773                         1.7            61.49                    0.571   \n",
       "151774                         1.7            61.49                    0.571   \n",
       "151775                         1.7            61.49                    0.571   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                        NaN                          NaN   \n",
       "1                                        NaN                          NaN   \n",
       "2                                        NaN                          NaN   \n",
       "3                                        NaN                          NaN   \n",
       "4                                        NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "151771                                   NaN                          NaN   \n",
       "151772                                   NaN                          NaN   \n",
       "151773                                   NaN                          NaN   \n",
       "151774                                   NaN                          NaN   \n",
       "151775                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0                    NaN                                      NaN  \n",
       "1                    NaN                                      NaN  \n",
       "2                    NaN                                      NaN  \n",
       "3                    NaN                                      NaN  \n",
       "4                    NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "151771               NaN                                      NaN  \n",
       "151772               NaN                                      NaN  \n",
       "151773               NaN                                      NaN  \n",
       "151774               NaN                                      NaN  \n",
       "151775               NaN                                      NaN  \n",
       "\n",
       "[151776 rows x 67 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing dependancies \n",
    "import pandas as pd\n",
    "from path import Path\n",
    "import time\n",
    "\n",
    "#Loading data\n",
    "file_path = Path(\"Resources/owid-covid-data.csv\")\n",
    "raw_covid_df = pd.read_csv(file_path)\n",
    "raw_covid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0860d737",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Date column to datetime\n",
    "raw_covid_df['date'] = pd.to_datetime(raw_covid_df['date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42377627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 151776 entries, 0 to 151775\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count   Dtype         \n",
      "---  ------                                      --------------   -----         \n",
      " 0   iso_code                                    151776 non-null  object        \n",
      " 1   continent                                   142626 non-null  object        \n",
      " 2   location                                    151776 non-null  object        \n",
      " 3   date                                        151776 non-null  datetime64[ns]\n",
      " 4   total_cases                                 149154 non-null  float64       \n",
      " 5   new_cases                                   149032 non-null  float64       \n",
      " 6   new_cases_smoothed                          147881 non-null  float64       \n",
      " 7   total_deaths                                131989 non-null  float64       \n",
      " 8   new_deaths                                  132139 non-null  float64       \n",
      " 9   new_deaths_smoothed                         132007 non-null  float64       \n",
      " 10  total_cases_per_million                     148458 non-null  float64       \n",
      " 11  new_cases_per_million                       148336 non-null  float64       \n",
      " 12  new_cases_smoothed_per_million              147190 non-null  float64       \n",
      " 13  total_deaths_per_million                    131306 non-null  float64       \n",
      " 14  new_deaths_per_million                      131456 non-null  float64       \n",
      " 15  new_deaths_smoothed_per_million             131324 non-null  float64       \n",
      " 16  reproduction_rate                           113675 non-null  float64       \n",
      " 17  icu_patients                                18542 non-null   float64       \n",
      " 18  icu_patients_per_million                    18542 non-null   float64       \n",
      " 19  hosp_patients                               20358 non-null   float64       \n",
      " 20  hosp_patients_per_million                   20358 non-null   float64       \n",
      " 21  weekly_icu_admissions                       4280 non-null    float64       \n",
      " 22  weekly_icu_admissions_per_million           4280 non-null    float64       \n",
      " 23  weekly_hosp_admissions                      8574 non-null    float64       \n",
      " 24  weekly_hosp_admissions_per_million          8574 non-null    float64       \n",
      " 25  new_tests                                   60362 non-null   float64       \n",
      " 26  total_tests                                 61737 non-null   float64       \n",
      " 27  total_tests_per_thousand                    61737 non-null   float64       \n",
      " 28  new_tests_per_thousand                      60362 non-null   float64       \n",
      " 29  new_tests_smoothed                          73687 non-null   float64       \n",
      " 30  new_tests_smoothed_per_thousand             72991 non-null   float64       \n",
      " 31  positive_rate                               68804 non-null   float64       \n",
      " 32  tests_per_case                              68141 non-null   float64       \n",
      " 33  tests_units                                 75151 non-null   object        \n",
      " 34  total_vaccinations                          38575 non-null   float64       \n",
      " 35  people_vaccinated                           37054 non-null   float64       \n",
      " 36  people_fully_vaccinated                     34271 non-null   float64       \n",
      " 37  total_boosters                              11955 non-null   float64       \n",
      " 38  new_vaccinations                            31955 non-null   float64       \n",
      " 39  new_vaccinations_smoothed                   70182 non-null   float64       \n",
      " 40  total_vaccinations_per_hundred              38575 non-null   float64       \n",
      " 41  people_vaccinated_per_hundred               37054 non-null   float64       \n",
      " 42  people_fully_vaccinated_per_hundred         34271 non-null   float64       \n",
      " 43  total_boosters_per_hundred                  11955 non-null   float64       \n",
      " 44  new_vaccinations_smoothed_per_million       70182 non-null   float64       \n",
      " 45  new_people_vaccinated_smoothed              69021 non-null   float64       \n",
      " 46  new_people_vaccinated_smoothed_per_hundred  69021 non-null   float64       \n",
      " 47  stringency_index                            118328 non-null  float64       \n",
      " 48  population                                  150763 non-null  float64       \n",
      " 49  population_density                          135119 non-null  float64       \n",
      " 50  median_age                                  126094 non-null  float64       \n",
      " 51  aged_65_older                               124724 non-null  float64       \n",
      " 52  aged_70_older                               125417 non-null  float64       \n",
      " 53  gdp_per_capita                              126501 non-null  float64       \n",
      " 54  extreme_poverty                             83508 non-null   float64       \n",
      " 55  cardiovasc_death_rate                       125103 non-null  float64       \n",
      " 56  diabetes_prevalence                         131514 non-null  float64       \n",
      " 57  female_smokers                              96901 non-null   float64       \n",
      " 58  male_smokers                                95505 non-null   float64       \n",
      " 59  handwashing_facilities                      62652 non-null   float64       \n",
      " 60  hospital_beds_per_thousand                  113027 non-null  float64       \n",
      " 61  life_expectancy                             141663 non-null  float64       \n",
      " 62  human_development_index                     124578 non-null  float64       \n",
      " 63  excess_mortality_cumulative_absolute        5234 non-null    float64       \n",
      " 64  excess_mortality_cumulative                 5234 non-null    float64       \n",
      " 65  excess_mortality                            5234 non-null    float64       \n",
      " 66  excess_mortality_cumulative_per_million     5234 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(62), object(4)\n",
      "memory usage: 77.6+ MB\n"
     ]
    }
   ],
   "source": [
    "raw_covid_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ed8fb5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                                        0\n",
       "continent                                    9150\n",
       "location                                        0\n",
       "date                                            0\n",
       "total_cases                                  2622\n",
       "                                            ...  \n",
       "human_development_index                     27198\n",
       "excess_mortality_cumulative_absolute       146542\n",
       "excess_mortality_cumulative                146542\n",
       "excess_mortality                           146542\n",
       "excess_mortality_cumulative_per_million    146542\n",
       "Length: 67, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_covid_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41b34f3",
   "metadata": {},
   "source": [
    "## Filtering the Raw DataFrame to Dates > 10/1/21 (Not used in the models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44faa248",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-10-02</td>\n",
       "      <td>155191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.000</td>\n",
       "      <td>7206.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>155191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.000</td>\n",
       "      <td>7206.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>155287.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>30.714</td>\n",
       "      <td>7212.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>155309.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.857</td>\n",
       "      <td>7214.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.857</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-10-06</td>\n",
       "      <td>155380.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>36.000</td>\n",
       "      <td>7220.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.286</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>207548.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>211728.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>213258.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21535 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  total_cases  new_cases  \\\n",
       "586         AFG      Asia  Afghanistan 2021-10-02     155191.0        0.0   \n",
       "587         AFG      Asia  Afghanistan 2021-10-03     155191.0        0.0   \n",
       "588         AFG      Asia  Afghanistan 2021-10-04     155287.0       96.0   \n",
       "589         AFG      Asia  Afghanistan 2021-10-05     155309.0       22.0   \n",
       "590         AFG      Asia  Afghanistan 2021-10-06     155380.0       71.0   \n",
       "...         ...       ...          ...        ...          ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29     207548.0        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     211728.0     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     213258.0     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01     214214.0      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02     214214.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "586                 33.000        7206.0         0.0                1.000   \n",
       "587                 33.000        7206.0         0.0                1.000   \n",
       "588                 30.714        7212.0         6.0                1.714   \n",
       "589                 30.857        7214.0         2.0                1.857   \n",
       "590                 36.000        7220.0         6.0                2.286   \n",
       "...                    ...           ...         ...                  ...   \n",
       "151771            1163.429        4940.0         0.0               16.000   \n",
       "151772            1483.429        4997.0        57.0               20.286   \n",
       "151773            1503.143        5004.0         7.0               19.000   \n",
       "151774            1495.429        5017.0        13.0               18.857   \n",
       "151775                 NaN        5017.0         NaN                  NaN   \n",
       "\n",
       "        ...  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "586     ...             NaN           NaN                  37.746   \n",
       "587     ...             NaN           NaN                  37.746   \n",
       "588     ...             NaN           NaN                  37.746   \n",
       "589     ...             NaN           NaN                  37.746   \n",
       "590     ...             NaN           NaN                  37.746   \n",
       "...     ...             ...           ...                     ...   \n",
       "151771  ...             1.6          30.7                  36.791   \n",
       "151772  ...             1.6          30.7                  36.791   \n",
       "151773  ...             1.6          30.7                  36.791   \n",
       "151774  ...             1.6          30.7                  36.791   \n",
       "151775  ...             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "586                            0.5            64.83                    0.511   \n",
       "587                            0.5            64.83                    0.511   \n",
       "588                            0.5            64.83                    0.511   \n",
       "589                            0.5            64.83                    0.511   \n",
       "590                            0.5            64.83                    0.511   \n",
       "...                            ...              ...                      ...   \n",
       "151771                         1.7            61.49                    0.571   \n",
       "151772                         1.7            61.49                    0.571   \n",
       "151773                         1.7            61.49                    0.571   \n",
       "151774                         1.7            61.49                    0.571   \n",
       "151775                         1.7            61.49                    0.571   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "586                                      NaN                          NaN   \n",
       "587                                      NaN                          NaN   \n",
       "588                                      NaN                          NaN   \n",
       "589                                      NaN                          NaN   \n",
       "590                                      NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "151771                                   NaN                          NaN   \n",
       "151772                                   NaN                          NaN   \n",
       "151773                                   NaN                          NaN   \n",
       "151774                                   NaN                          NaN   \n",
       "151775                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "586                  NaN                                      NaN  \n",
       "587                  NaN                                      NaN  \n",
       "588                  NaN                                      NaN  \n",
       "589                  NaN                                      NaN  \n",
       "590                  NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "151771               NaN                                      NaN  \n",
       "151772               NaN                                      NaN  \n",
       "151773               NaN                                      NaN  \n",
       "151774               NaN                                      NaN  \n",
       "151775               NaN                                      NaN  \n",
       "\n",
       "[21535 rows x 67 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reducing dataset to last 3 months - NOT USED IN MODELS\n",
    "Covid_Stats_Last3Months = raw_covid_df[raw_covid_df['date']>'2021-10-01']\n",
    "Covid_Stats_Last3Months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a51ba71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                                       0\n",
       "continent                                   1209\n",
       "location                                       0\n",
       "date                                           0\n",
       "total_cases                                  358\n",
       "                                           ...  \n",
       "human_development_index                     3958\n",
       "excess_mortality_cumulative_absolute       21117\n",
       "excess_mortality_cumulative                21117\n",
       "excess_mortality                           21117\n",
       "excess_mortality_cumulative_per_million    21117\n",
       "Length: 67, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking nulls\n",
    "Covid_Stats_Last3Months.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3b1ee94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21535 entries, 586 to 151775\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count  Dtype         \n",
      "---  ------                                      --------------  -----         \n",
      " 0   iso_code                                    21535 non-null  object        \n",
      " 1   continent                                   20326 non-null  object        \n",
      " 2   location                                    21535 non-null  object        \n",
      " 3   date                                        21535 non-null  datetime64[ns]\n",
      " 4   total_cases                                 21177 non-null  float64       \n",
      " 5   new_cases                                   21060 non-null  float64       \n",
      " 6   new_cases_smoothed                          21055 non-null  float64       \n",
      " 7   total_deaths                                19909 non-null  float64       \n",
      " 8   new_deaths                                  19864 non-null  float64       \n",
      " 9   new_deaths_smoothed                         19864 non-null  float64       \n",
      " 10  total_cases_per_million                     21084 non-null  float64       \n",
      " 11  new_cases_per_million                       20967 non-null  float64       \n",
      " 12  new_cases_smoothed_per_million              20962 non-null  float64       \n",
      " 13  total_deaths_per_million                    19816 non-null  float64       \n",
      " 14  new_deaths_per_million                      19771 non-null  float64       \n",
      " 15  new_deaths_smoothed_per_million             19771 non-null  float64       \n",
      " 16  reproduction_rate                           16095 non-null  float64       \n",
      " 17  icu_patients                                2604 non-null   float64       \n",
      " 18  icu_patients_per_million                    2604 non-null   float64       \n",
      " 19  hosp_patients                               2748 non-null   float64       \n",
      " 20  hosp_patients_per_million                   2748 non-null   float64       \n",
      " 21  weekly_icu_admissions                       766 non-null    float64       \n",
      " 22  weekly_icu_admissions_per_million           766 non-null    float64       \n",
      " 23  weekly_hosp_admissions                      1263 non-null   float64       \n",
      " 24  weekly_hosp_admissions_per_million          1263 non-null   float64       \n",
      " 25  new_tests                                   7312 non-null   float64       \n",
      " 26  total_tests                                 7804 non-null   float64       \n",
      " 27  total_tests_per_thousand                    7804 non-null   float64       \n",
      " 28  new_tests_per_thousand                      7312 non-null   float64       \n",
      " 29  new_tests_smoothed                          10123 non-null  float64       \n",
      " 30  new_tests_smoothed_per_thousand             10035 non-null  float64       \n",
      " 31  positive_rate                               9659 non-null   float64       \n",
      " 32  tests_per_case                              9644 non-null   float64       \n",
      " 33  tests_units                                 10275 non-null  object        \n",
      " 34  total_vaccinations                          10222 non-null  float64       \n",
      " 35  people_vaccinated                           9675 non-null   float64       \n",
      " 36  people_fully_vaccinated                     9889 non-null   float64       \n",
      " 37  total_boosters                              5582 non-null   float64       \n",
      " 38  new_vaccinations                            8227 non-null   float64       \n",
      " 39  new_vaccinations_smoothed                   18985 non-null  float64       \n",
      " 40  total_vaccinations_per_hundred              10222 non-null  float64       \n",
      " 41  people_vaccinated_per_hundred               9675 non-null   float64       \n",
      " 42  people_fully_vaccinated_per_hundred         9889 non-null   float64       \n",
      " 43  total_boosters_per_hundred                  5582 non-null   float64       \n",
      " 44  new_vaccinations_smoothed_per_million       18985 non-null  float64       \n",
      " 45  new_people_vaccinated_smoothed              18702 non-null  float64       \n",
      " 46  new_people_vaccinated_smoothed_per_hundred  18702 non-null  float64       \n",
      " 47  stringency_index                            14469 non-null  float64       \n",
      " 48  population                                  21386 non-null  float64       \n",
      " 49  population_density                          19076 non-null  float64       \n",
      " 50  median_age                                  17670 non-null  float64       \n",
      " 51  aged_65_older                               17484 non-null  float64       \n",
      " 52  aged_70_older                               17577 non-null  float64       \n",
      " 53  gdp_per_capita                              17867 non-null  float64       \n",
      " 54  extreme_poverty                             11646 non-null  float64       \n",
      " 55  cardiovasc_death_rate                       17577 non-null  float64       \n",
      " 56  diabetes_prevalence                         18528 non-null  float64       \n",
      " 57  female_smokers                              13578 non-null  float64       \n",
      " 58  male_smokers                                13392 non-null  float64       \n",
      " 59  handwashing_facilities                      8835 non-null   float64       \n",
      " 60  hospital_beds_per_thousand                  15810 non-null  float64       \n",
      " 61  life_expectancy                             20110 non-null  float64       \n",
      " 62  human_development_index                     17577 non-null  float64       \n",
      " 63  excess_mortality_cumulative_absolute        418 non-null    float64       \n",
      " 64  excess_mortality_cumulative                 418 non-null    float64       \n",
      " 65  excess_mortality                            418 non-null    float64       \n",
      " 66  excess_mortality_cumulative_per_million     418 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(62), object(4)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "Covid_Stats_Last3Months.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ad396f",
   "metadata": {},
   "source": [
    "## Preprocessing Data for Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f65c1da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>207548.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>211728.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>213258.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...        ...          ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29     207548.0        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     211728.0     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     213258.0     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01     214214.0      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02     214214.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN           NaN         NaN                  NaN   \n",
       "1                      NaN           NaN         NaN                  NaN   \n",
       "2                      NaN           NaN         NaN                  NaN   \n",
       "3                      NaN           NaN         NaN                  NaN   \n",
       "4                      NaN           NaN         NaN                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "151771            1163.429        4940.0         0.0               16.000   \n",
       "151772            1483.429        4997.0        57.0               20.286   \n",
       "151773            1503.143        5004.0         7.0               19.000   \n",
       "151774            1495.429        5017.0        13.0               18.857   \n",
       "151775                 NaN        5017.0         NaN                  NaN   \n",
       "\n",
       "        ...  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0       ...             NaN           NaN                  37.746   \n",
       "1       ...             NaN           NaN                  37.746   \n",
       "2       ...             NaN           NaN                  37.746   \n",
       "3       ...             NaN           NaN                  37.746   \n",
       "4       ...             NaN           NaN                  37.746   \n",
       "...     ...             ...           ...                     ...   \n",
       "151771  ...             1.6          30.7                  36.791   \n",
       "151772  ...             1.6          30.7                  36.791   \n",
       "151773  ...             1.6          30.7                  36.791   \n",
       "151774  ...             1.6          30.7                  36.791   \n",
       "151775  ...             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                              0.5            64.83                    0.511   \n",
       "1                              0.5            64.83                    0.511   \n",
       "2                              0.5            64.83                    0.511   \n",
       "3                              0.5            64.83                    0.511   \n",
       "4                              0.5            64.83                    0.511   \n",
       "...                            ...              ...                      ...   \n",
       "151771                         1.7            61.49                    0.571   \n",
       "151772                         1.7            61.49                    0.571   \n",
       "151773                         1.7            61.49                    0.571   \n",
       "151774                         1.7            61.49                    0.571   \n",
       "151775                         1.7            61.49                    0.571   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                        NaN                          NaN   \n",
       "1                                        NaN                          NaN   \n",
       "2                                        NaN                          NaN   \n",
       "3                                        NaN                          NaN   \n",
       "4                                        NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "151771                                   NaN                          NaN   \n",
       "151772                                   NaN                          NaN   \n",
       "151773                                   NaN                          NaN   \n",
       "151774                                   NaN                          NaN   \n",
       "151775                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0                    NaN                                      NaN  \n",
       "1                    NaN                                      NaN  \n",
       "2                    NaN                                      NaN  \n",
       "3                    NaN                                      NaN  \n",
       "4                    NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "151771               NaN                                      NaN  \n",
       "151772               NaN                                      NaN  \n",
       "151773               NaN                                      NaN  \n",
       "151774               NaN                                      NaN  \n",
       "151775               NaN                                      NaN  \n",
       "\n",
       "[141649 rows x 67 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - Removing unwanted rows having 'OWID' in their 'iso_code'\n",
    "Iso_Cleaned_df = raw_covid_df[~raw_covid_df['iso_code'].astype(str).str.startswith('OWID')]\n",
    "Iso_Cleaned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e4737e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>207548.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>211728.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>213258.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...        ...          ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29     207548.0        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     211728.0     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     213258.0     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01     214214.0      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02     214214.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN           NaN         NaN                  NaN   \n",
       "1                      NaN           NaN         NaN                  NaN   \n",
       "2                      NaN           NaN         NaN                  NaN   \n",
       "3                      NaN           NaN         NaN                  NaN   \n",
       "4                      NaN           NaN         NaN                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "151771            1163.429        4940.0         0.0               16.000   \n",
       "151772            1483.429        4997.0        57.0               20.286   \n",
       "151773            1503.143        5004.0         7.0               19.000   \n",
       "151774            1495.429        5017.0        13.0               18.857   \n",
       "151775                 NaN        5017.0         NaN                  NaN   \n",
       "\n",
       "        ...  gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0       ...        1803.987              NaN                597.029   \n",
       "1       ...        1803.987              NaN                597.029   \n",
       "2       ...        1803.987              NaN                597.029   \n",
       "3       ...        1803.987              NaN                597.029   \n",
       "4       ...        1803.987              NaN                597.029   \n",
       "...     ...             ...              ...                    ...   \n",
       "151771  ...        1899.775             21.4                307.846   \n",
       "151772  ...        1899.775             21.4                307.846   \n",
       "151773  ...        1899.775             21.4                307.846   \n",
       "151774  ...        1899.775             21.4                307.846   \n",
       "151775  ...        1899.775             21.4                307.846   \n",
       "\n",
       "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                      9.59             NaN           NaN   \n",
       "1                      9.59             NaN           NaN   \n",
       "2                      9.59             NaN           NaN   \n",
       "3                      9.59             NaN           NaN   \n",
       "4                      9.59             NaN           NaN   \n",
       "...                     ...             ...           ...   \n",
       "151771                 1.82             1.6          30.7   \n",
       "151772                 1.82             1.6          30.7   \n",
       "151773                 1.82             1.6          30.7   \n",
       "151774                 1.82             1.6          30.7   \n",
       "151775                 1.82             1.6          30.7   \n",
       "\n",
       "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                       37.746                         0.5            64.83   \n",
       "1                       37.746                         0.5            64.83   \n",
       "2                       37.746                         0.5            64.83   \n",
       "3                       37.746                         0.5            64.83   \n",
       "4                       37.746                         0.5            64.83   \n",
       "...                        ...                         ...              ...   \n",
       "151771                  36.791                         1.7            61.49   \n",
       "151772                  36.791                         1.7            61.49   \n",
       "151773                  36.791                         1.7            61.49   \n",
       "151774                  36.791                         1.7            61.49   \n",
       "151775                  36.791                         1.7            61.49   \n",
       "\n",
       "        human_development_index  \n",
       "0                         0.511  \n",
       "1                         0.511  \n",
       "2                         0.511  \n",
       "3                         0.511  \n",
       "4                         0.511  \n",
       "...                         ...  \n",
       "151771                    0.571  \n",
       "151772                    0.571  \n",
       "151773                    0.571  \n",
       "151774                    0.571  \n",
       "151775                    0.571  \n",
       "\n",
       "[141649 rows x 63 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping 'excess' columns'\n",
    "dropped_mortality_df = Iso_Cleaned_df.drop(['excess_mortality_cumulative_absolute','excess_mortality_cumulative','excess_mortality','excess_mortality_cumulative_per_million'], axis=1)\n",
    "dropped_mortality_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a91c03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 141649 entries, 0 to 151775\n",
      "Data columns (total 63 columns):\n",
      " #   Column                                      Non-Null Count   Dtype         \n",
      "---  ------                                      --------------   -----         \n",
      " 0   iso_code                                    141649 non-null  object        \n",
      " 1   continent                                   141649 non-null  object        \n",
      " 2   location                                    141649 non-null  object        \n",
      " 3   date                                        141649 non-null  datetime64[ns]\n",
      " 4   total_cases                                 139351 non-null  float64       \n",
      " 5   new_cases                                   139222 non-null  float64       \n",
      " 6   new_cases_smoothed                          138153 non-null  float64       \n",
      " 7   total_deaths                                122401 non-null  float64       \n",
      " 8   new_deaths                                  122351 non-null  float64       \n",
      " 9   new_deaths_smoothed                         122291 non-null  float64       \n",
      " 10  total_cases_per_million                     139351 non-null  float64       \n",
      " 11  new_cases_per_million                       139222 non-null  float64       \n",
      " 12  new_cases_smoothed_per_million              138153 non-null  float64       \n",
      " 13  total_deaths_per_million                    122401 non-null  float64       \n",
      " 14  new_deaths_per_million                      122351 non-null  float64       \n",
      " 15  new_deaths_smoothed_per_million             122291 non-null  float64       \n",
      " 16  reproduction_rate                           112334 non-null  float64       \n",
      " 17  icu_patients                                18542 non-null   float64       \n",
      " 18  icu_patients_per_million                    18542 non-null   float64       \n",
      " 19  hosp_patients                               20358 non-null   float64       \n",
      " 20  hosp_patients_per_million                   20358 non-null   float64       \n",
      " 21  weekly_icu_admissions                       4280 non-null    float64       \n",
      " 22  weekly_icu_admissions_per_million           4280 non-null    float64       \n",
      " 23  weekly_hosp_admissions                      8574 non-null    float64       \n",
      " 24  weekly_hosp_admissions_per_million          8574 non-null    float64       \n",
      " 25  new_tests                                   60281 non-null   float64       \n",
      " 26  total_tests                                 61612 non-null   float64       \n",
      " 27  total_tests_per_thousand                    61612 non-null   float64       \n",
      " 28  new_tests_per_thousand                      60281 non-null   float64       \n",
      " 29  new_tests_smoothed                          72777 non-null   float64       \n",
      " 30  new_tests_smoothed_per_thousand             72777 non-null   float64       \n",
      " 31  positive_rate                               68590 non-null   float64       \n",
      " 32  tests_per_case                              67927 non-null   float64       \n",
      " 33  tests_units                                 74930 non-null   object        \n",
      " 34  total_vaccinations                          33957 non-null   float64       \n",
      " 35  people_vaccinated                           32441 non-null   float64       \n",
      " 36  people_fully_vaccinated                     29833 non-null   float64       \n",
      " 37  total_boosters                              8806 non-null    float64       \n",
      " 38  new_vaccinations                            27426 non-null   float64       \n",
      " 39  new_vaccinations_smoothed                   65106 non-null   float64       \n",
      " 40  total_vaccinations_per_hundred              33957 non-null   float64       \n",
      " 41  people_vaccinated_per_hundred               32441 non-null   float64       \n",
      " 42  people_fully_vaccinated_per_hundred         29833 non-null   float64       \n",
      " 43  total_boosters_per_hundred                  8806 non-null    float64       \n",
      " 44  new_vaccinations_smoothed_per_million       65106 non-null   float64       \n",
      " 45  new_people_vaccinated_smoothed              64015 non-null   float64       \n",
      " 46  new_people_vaccinated_smoothed_per_hundred  64015 non-null   float64       \n",
      " 47  stringency_index                            117682 non-null  float64       \n",
      " 48  population                                  141649 non-null  float64       \n",
      " 49  population_density                          133747 non-null  float64       \n",
      " 50  median_age                                  125382 non-null  float64       \n",
      " 51  aged_65_older                               124012 non-null  float64       \n",
      " 52  aged_70_older                               124705 non-null  float64       \n",
      " 53  gdp_per_capita                              125129 non-null  float64       \n",
      " 54  extreme_poverty                             82136 non-null   float64       \n",
      " 55  cardiovasc_death_rate                       124391 non-null  float64       \n",
      " 56  diabetes_prevalence                         130802 non-null  float64       \n",
      " 57  female_smokers                              96189 non-null   float64       \n",
      " 58  male_smokers                                94793 non-null   float64       \n",
      " 59  handwashing_facilities                      61940 non-null   float64       \n",
      " 60  hospital_beds_per_thousand                  112315 non-null  float64       \n",
      " 61  life_expectancy                             140951 non-null  float64       \n",
      " 62  human_development_index                     123866 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(58), object(4)\n",
      "memory usage: 69.2+ MB\n"
     ]
    }
   ],
   "source": [
    "dropped_mortality_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "859b6510",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
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       "      <th>new_cases</th>\n",
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       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.511</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
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       "      <td>207548.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>4940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
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       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>211728.0</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
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       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
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       "      <td>1530.0</td>\n",
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       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
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       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>214214.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...        ...          ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29     207548.0        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     211728.0     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     213258.0     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01     214214.0      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02     214214.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN           NaN         NaN                  NaN   \n",
       "1                      NaN           NaN         NaN                  NaN   \n",
       "2                      NaN           NaN         NaN                  NaN   \n",
       "3                      NaN           NaN         NaN                  NaN   \n",
       "4                      NaN           NaN         NaN                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "151771            1163.429        4940.0         0.0               16.000   \n",
       "151772            1483.429        4997.0        57.0               20.286   \n",
       "151773            1503.143        5004.0         7.0               19.000   \n",
       "151774            1495.429        5017.0        13.0               18.857   \n",
       "151775                 NaN        5017.0         NaN                  NaN   \n",
       "\n",
       "        ...  gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0       ...        1803.987              NaN                597.029   \n",
       "1       ...        1803.987              NaN                597.029   \n",
       "2       ...        1803.987              NaN                597.029   \n",
       "3       ...        1803.987              NaN                597.029   \n",
       "4       ...        1803.987              NaN                597.029   \n",
       "...     ...             ...              ...                    ...   \n",
       "151771  ...        1899.775             21.4                307.846   \n",
       "151772  ...        1899.775             21.4                307.846   \n",
       "151773  ...        1899.775             21.4                307.846   \n",
       "151774  ...        1899.775             21.4                307.846   \n",
       "151775  ...        1899.775             21.4                307.846   \n",
       "\n",
       "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                      9.59             NaN           NaN   \n",
       "1                      9.59             NaN           NaN   \n",
       "2                      9.59             NaN           NaN   \n",
       "3                      9.59             NaN           NaN   \n",
       "4                      9.59             NaN           NaN   \n",
       "...                     ...             ...           ...   \n",
       "151771                 1.82             1.6          30.7   \n",
       "151772                 1.82             1.6          30.7   \n",
       "151773                 1.82             1.6          30.7   \n",
       "151774                 1.82             1.6          30.7   \n",
       "151775                 1.82             1.6          30.7   \n",
       "\n",
       "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                       37.746                         0.5            64.83   \n",
       "1                       37.746                         0.5            64.83   \n",
       "2                       37.746                         0.5            64.83   \n",
       "3                       37.746                         0.5            64.83   \n",
       "4                       37.746                         0.5            64.83   \n",
       "...                        ...                         ...              ...   \n",
       "151771                  36.791                         1.7            61.49   \n",
       "151772                  36.791                         1.7            61.49   \n",
       "151773                  36.791                         1.7            61.49   \n",
       "151774                  36.791                         1.7            61.49   \n",
       "151775                  36.791                         1.7            61.49   \n",
       "\n",
       "        human_development_index  \n",
       "0                         0.511  \n",
       "1                         0.511  \n",
       "2                         0.511  \n",
       "3                         0.511  \n",
       "4                         0.511  \n",
       "...                         ...  \n",
       "151771                    0.571  \n",
       "151772                    0.571  \n",
       "151773                    0.571  \n",
       "151774                    0.571  \n",
       "151775                    0.571  \n",
       "\n",
       "[141649 rows x 58 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping columns with less than 10k rows\n",
    "dropped_excess_null_df=dropped_mortality_df.drop(['weekly_icu_admissions','weekly_icu_admissions_per_million','weekly_hosp_admissions','weekly_hosp_admissions_per_million','total_boosters_per_hundred'], axis=1)\n",
    "dropped_excess_null_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d4ba202",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
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       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>13752.031</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>14028.996</td>\n",
       "      <td>276.965</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>14130.373</td>\n",
       "      <td>101.377</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>14193.717</td>\n",
       "      <td>63.344</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14193.717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28        0.0   \n",
       "...         ...       ...          ...        ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02        NaN   \n",
       "\n",
       "        new_cases_smoothed  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN         NaN                  NaN   \n",
       "1                      NaN         NaN                  NaN   \n",
       "2                      NaN         NaN                  NaN   \n",
       "3                      NaN         NaN                  NaN   \n",
       "4                      NaN         NaN                  NaN   \n",
       "...                    ...         ...                  ...   \n",
       "151771            1163.429         0.0               16.000   \n",
       "151772            1483.429        57.0               20.286   \n",
       "151773            1503.143         7.0               19.000   \n",
       "151774            1495.429        13.0               18.857   \n",
       "151775                 NaN         NaN                  NaN   \n",
       "\n",
       "        total_cases_per_million  new_cases_per_million  ...  gdp_per_capita  \\\n",
       "0                         0.126                  0.126  ...        1803.987   \n",
       "1                         0.126                  0.000  ...        1803.987   \n",
       "2                         0.126                  0.000  ...        1803.987   \n",
       "3                         0.126                  0.000  ...        1803.987   \n",
       "4                         0.126                  0.000  ...        1803.987   \n",
       "...                         ...                    ...  ...             ...   \n",
       "151771                13752.031                  0.000  ...        1899.775   \n",
       "151772                14028.996                276.965  ...        1899.775   \n",
       "151773                14130.373                101.377  ...        1899.775   \n",
       "151774                14193.717                 63.344  ...        1899.775   \n",
       "151775                14193.717                    NaN  ...        1899.775   \n",
       "\n",
       "        extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0                   NaN                597.029                 9.59   \n",
       "1                   NaN                597.029                 9.59   \n",
       "2                   NaN                597.029                 9.59   \n",
       "3                   NaN                597.029                 9.59   \n",
       "4                   NaN                597.029                 9.59   \n",
       "...                 ...                    ...                  ...   \n",
       "151771             21.4                307.846                 1.82   \n",
       "151772             21.4                307.846                 1.82   \n",
       "151773             21.4                307.846                 1.82   \n",
       "151774             21.4                307.846                 1.82   \n",
       "151775             21.4                307.846                 1.82   \n",
       "\n",
       "        female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0                  NaN           NaN                  37.746   \n",
       "1                  NaN           NaN                  37.746   \n",
       "2                  NaN           NaN                  37.746   \n",
       "3                  NaN           NaN                  37.746   \n",
       "4                  NaN           NaN                  37.746   \n",
       "...                ...           ...                     ...   \n",
       "151771             1.6          30.7                  36.791   \n",
       "151772             1.6          30.7                  36.791   \n",
       "151773             1.6          30.7                  36.791   \n",
       "151774             1.6          30.7                  36.791   \n",
       "151775             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \n",
       "0                              0.5            64.83                    0.511  \n",
       "1                              0.5            64.83                    0.511  \n",
       "2                              0.5            64.83                    0.511  \n",
       "3                              0.5            64.83                    0.511  \n",
       "4                              0.5            64.83                    0.511  \n",
       "...                            ...              ...                      ...  \n",
       "151771                         1.7            61.49                    0.571  \n",
       "151772                         1.7            61.49                    0.571  \n",
       "151773                         1.7            61.49                    0.571  \n",
       "151774                         1.7            61.49                    0.571  \n",
       "151775                         1.7            61.49                    0.571  \n",
       "\n",
       "[141649 rows x 53 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping cumulative columns\n",
    "taken_out_cumulative_df=dropped_excess_null_df.drop(['total_cases','total_deaths','total_tests','total_deaths_per_million','total_vaccinations_per_hundred'], axis=1)\n",
    "taken_out_cumulative_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5e46045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 141649 entries, 0 to 151775\n",
      "Data columns (total 53 columns):\n",
      " #   Column                                      Non-Null Count   Dtype         \n",
      "---  ------                                      --------------   -----         \n",
      " 0   iso_code                                    141649 non-null  object        \n",
      " 1   continent                                   141649 non-null  object        \n",
      " 2   location                                    141649 non-null  object        \n",
      " 3   date                                        141649 non-null  datetime64[ns]\n",
      " 4   new_cases                                   139222 non-null  float64       \n",
      " 5   new_cases_smoothed                          138153 non-null  float64       \n",
      " 6   new_deaths                                  122351 non-null  float64       \n",
      " 7   new_deaths_smoothed                         122291 non-null  float64       \n",
      " 8   total_cases_per_million                     139351 non-null  float64       \n",
      " 9   new_cases_per_million                       139222 non-null  float64       \n",
      " 10  new_cases_smoothed_per_million              138153 non-null  float64       \n",
      " 11  new_deaths_per_million                      122351 non-null  float64       \n",
      " 12  new_deaths_smoothed_per_million             122291 non-null  float64       \n",
      " 13  reproduction_rate                           112334 non-null  float64       \n",
      " 14  icu_patients                                18542 non-null   float64       \n",
      " 15  icu_patients_per_million                    18542 non-null   float64       \n",
      " 16  hosp_patients                               20358 non-null   float64       \n",
      " 17  hosp_patients_per_million                   20358 non-null   float64       \n",
      " 18  new_tests                                   60281 non-null   float64       \n",
      " 19  total_tests_per_thousand                    61612 non-null   float64       \n",
      " 20  new_tests_per_thousand                      60281 non-null   float64       \n",
      " 21  new_tests_smoothed                          72777 non-null   float64       \n",
      " 22  new_tests_smoothed_per_thousand             72777 non-null   float64       \n",
      " 23  positive_rate                               68590 non-null   float64       \n",
      " 24  tests_per_case                              67927 non-null   float64       \n",
      " 25  tests_units                                 74930 non-null   object        \n",
      " 26  total_vaccinations                          33957 non-null   float64       \n",
      " 27  people_vaccinated                           32441 non-null   float64       \n",
      " 28  people_fully_vaccinated                     29833 non-null   float64       \n",
      " 29  total_boosters                              8806 non-null    float64       \n",
      " 30  new_vaccinations                            27426 non-null   float64       \n",
      " 31  new_vaccinations_smoothed                   65106 non-null   float64       \n",
      " 32  people_vaccinated_per_hundred               32441 non-null   float64       \n",
      " 33  people_fully_vaccinated_per_hundred         29833 non-null   float64       \n",
      " 34  new_vaccinations_smoothed_per_million       65106 non-null   float64       \n",
      " 35  new_people_vaccinated_smoothed              64015 non-null   float64       \n",
      " 36  new_people_vaccinated_smoothed_per_hundred  64015 non-null   float64       \n",
      " 37  stringency_index                            117682 non-null  float64       \n",
      " 38  population                                  141649 non-null  float64       \n",
      " 39  population_density                          133747 non-null  float64       \n",
      " 40  median_age                                  125382 non-null  float64       \n",
      " 41  aged_65_older                               124012 non-null  float64       \n",
      " 42  aged_70_older                               124705 non-null  float64       \n",
      " 43  gdp_per_capita                              125129 non-null  float64       \n",
      " 44  extreme_poverty                             82136 non-null   float64       \n",
      " 45  cardiovasc_death_rate                       124391 non-null  float64       \n",
      " 46  diabetes_prevalence                         130802 non-null  float64       \n",
      " 47  female_smokers                              96189 non-null   float64       \n",
      " 48  male_smokers                                94793 non-null   float64       \n",
      " 49  handwashing_facilities                      61940 non-null   float64       \n",
      " 50  hospital_beds_per_thousand                  112315 non-null  float64       \n",
      " 51  life_expectancy                             140951 non-null  float64       \n",
      " 52  human_development_index                     123866 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(48), object(4)\n",
      "memory usage: 58.4+ MB\n"
     ]
    }
   ],
   "source": [
    "taken_out_cumulative_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c4b9ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                                           0\n",
       "continent                                          0\n",
       "location                                           0\n",
       "date                                               0\n",
       "new_cases                                       2427\n",
       "new_cases_smoothed                              3496\n",
       "new_deaths                                     19298\n",
       "new_deaths_smoothed                            19358\n",
       "total_cases_per_million                         2298\n",
       "new_cases_per_million                           2427\n",
       "new_cases_smoothed_per_million                  3496\n",
       "new_deaths_per_million                         19298\n",
       "new_deaths_smoothed_per_million                19358\n",
       "reproduction_rate                              29315\n",
       "icu_patients                                  123107\n",
       "icu_patients_per_million                      123107\n",
       "hosp_patients                                 121291\n",
       "hosp_patients_per_million                     121291\n",
       "new_tests                                      81368\n",
       "total_tests_per_thousand                       80037\n",
       "new_tests_per_thousand                         81368\n",
       "new_tests_smoothed                             68872\n",
       "new_tests_smoothed_per_thousand                68872\n",
       "positive_rate                                  73059\n",
       "tests_per_case                                 73722\n",
       "tests_units                                    66719\n",
       "total_vaccinations                            107692\n",
       "people_vaccinated                             109208\n",
       "people_fully_vaccinated                       111816\n",
       "total_boosters                                132843\n",
       "new_vaccinations                              114223\n",
       "new_vaccinations_smoothed                      76543\n",
       "people_vaccinated_per_hundred                 109208\n",
       "people_fully_vaccinated_per_hundred           111816\n",
       "new_vaccinations_smoothed_per_million          76543\n",
       "new_people_vaccinated_smoothed                 77634\n",
       "new_people_vaccinated_smoothed_per_hundred     77634\n",
       "stringency_index                               23967\n",
       "population                                         0\n",
       "population_density                              7902\n",
       "median_age                                     16267\n",
       "aged_65_older                                  17637\n",
       "aged_70_older                                  16944\n",
       "gdp_per_capita                                 16520\n",
       "extreme_poverty                                59513\n",
       "cardiovasc_death_rate                          17258\n",
       "diabetes_prevalence                            10847\n",
       "female_smokers                                 45460\n",
       "male_smokers                                   46856\n",
       "handwashing_facilities                         79709\n",
       "hospital_beds_per_thousand                     29334\n",
       "life_expectancy                                  698\n",
       "human_development_index                        17783\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taken_out_cumulative_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ed6d271",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.126</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1163.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>13752.031</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>1483.429</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20.286</td>\n",
       "      <td>14028.996</td>\n",
       "      <td>276.965</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1503.143</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.000</td>\n",
       "      <td>14130.373</td>\n",
       "      <td>101.377</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>956.0</td>\n",
       "      <td>1495.429</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.857</td>\n",
       "      <td>14193.717</td>\n",
       "      <td>63.344</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14193.717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28        0.0   \n",
       "...         ...       ...          ...        ...        ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29        0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     4180.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     1530.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01      956.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02        NaN   \n",
       "\n",
       "        new_cases_smoothed  new_deaths  new_deaths_smoothed  \\\n",
       "0                      NaN         NaN                  NaN   \n",
       "1                      NaN         NaN                  NaN   \n",
       "2                      NaN         NaN                  NaN   \n",
       "3                      NaN         NaN                  NaN   \n",
       "4                      NaN         NaN                  NaN   \n",
       "...                    ...         ...                  ...   \n",
       "151771            1163.429         0.0               16.000   \n",
       "151772            1483.429        57.0               20.286   \n",
       "151773            1503.143         7.0               19.000   \n",
       "151774            1495.429        13.0               18.857   \n",
       "151775                 NaN         NaN                  NaN   \n",
       "\n",
       "        total_cases_per_million  new_cases_per_million  ...  aged_70_older  \\\n",
       "0                         0.126                  0.126  ...          1.337   \n",
       "1                         0.126                  0.000  ...          1.337   \n",
       "2                         0.126                  0.000  ...          1.337   \n",
       "3                         0.126                  0.000  ...          1.337   \n",
       "4                         0.126                  0.000  ...          1.337   \n",
       "...                         ...                    ...  ...            ...   \n",
       "151771                13752.031                  0.000  ...          1.882   \n",
       "151772                14028.996                276.965  ...          1.882   \n",
       "151773                14130.373                101.377  ...          1.882   \n",
       "151774                14193.717                 63.344  ...          1.882   \n",
       "151775                14193.717                    NaN  ...          1.882   \n",
       "\n",
       "        gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0             1803.987              NaN                597.029   \n",
       "1             1803.987              NaN                597.029   \n",
       "2             1803.987              NaN                597.029   \n",
       "3             1803.987              NaN                597.029   \n",
       "4             1803.987              NaN                597.029   \n",
       "...                ...              ...                    ...   \n",
       "151771        1899.775             21.4                307.846   \n",
       "151772        1899.775             21.4                307.846   \n",
       "151773        1899.775             21.4                307.846   \n",
       "151774        1899.775             21.4                307.846   \n",
       "151775        1899.775             21.4                307.846   \n",
       "\n",
       "        diabetes_prevalence  female_smokers male_smokers  \\\n",
       "0                      9.59             NaN          NaN   \n",
       "1                      9.59             NaN          NaN   \n",
       "2                      9.59             NaN          NaN   \n",
       "3                      9.59             NaN          NaN   \n",
       "4                      9.59             NaN          NaN   \n",
       "...                     ...             ...          ...   \n",
       "151771                 1.82             1.6         30.7   \n",
       "151772                 1.82             1.6         30.7   \n",
       "151773                 1.82             1.6         30.7   \n",
       "151774                 1.82             1.6         30.7   \n",
       "151775                 1.82             1.6         30.7   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \n",
       "0                              0.5            64.83                    0.511  \n",
       "1                              0.5            64.83                    0.511  \n",
       "2                              0.5            64.83                    0.511  \n",
       "3                              0.5            64.83                    0.511  \n",
       "4                              0.5            64.83                    0.511  \n",
       "...                            ...              ...                      ...  \n",
       "151771                         1.7            61.49                    0.571  \n",
       "151772                         1.7            61.49                    0.571  \n",
       "151773                         1.7            61.49                    0.571  \n",
       "151774                         1.7            61.49                    0.571  \n",
       "151775                         1.7            61.49                    0.571  \n",
       "\n",
       "[141649 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping columns with its rows only half populated\n",
    "dropped_nulls_df = taken_out_cumulative_df.loc[:, taken_out_cumulative_df.isnull().mean() < .5]\n",
    "dropped_nulls_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f92fb650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                               0\n",
       "continent                              0\n",
       "location                               0\n",
       "date                                   0\n",
       "new_cases                           2427\n",
       "new_cases_smoothed                  3496\n",
       "new_deaths                         19298\n",
       "new_deaths_smoothed                19358\n",
       "total_cases_per_million             2298\n",
       "new_cases_per_million               2427\n",
       "new_cases_smoothed_per_million      3496\n",
       "new_deaths_per_million             19298\n",
       "new_deaths_smoothed_per_million    19358\n",
       "reproduction_rate                  29315\n",
       "new_tests_smoothed                 68872\n",
       "new_tests_smoothed_per_thousand    68872\n",
       "tests_units                        66719\n",
       "stringency_index                   23967\n",
       "population                             0\n",
       "population_density                  7902\n",
       "median_age                         16267\n",
       "aged_65_older                      17637\n",
       "aged_70_older                      16944\n",
       "gdp_per_capita                     16520\n",
       "extreme_poverty                    59513\n",
       "cardiovasc_death_rate              17258\n",
       "diabetes_prevalence                10847\n",
       "female_smokers                     45460\n",
       "male_smokers                       46856\n",
       "hospital_beds_per_thousand         29334\n",
       "life_expectancy                      698\n",
       "human_development_index            17783\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_nulls_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15ca58fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>tests_units</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151771</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4563.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151772</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>98.291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151773</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>99.598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5259.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151774</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>956.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>99.086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5358.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151775</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5502.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141649 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  new_cases  new_deaths  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24        5.0         NaN   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25        0.0         NaN   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26        0.0         NaN   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27        0.0         NaN   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28        0.0         NaN   \n",
       "...         ...       ...          ...        ...        ...         ...   \n",
       "151771      ZWE    Africa     Zimbabwe 2021-12-29        0.0         0.0   \n",
       "151772      ZWE    Africa     Zimbabwe 2021-12-30     4180.0        57.0   \n",
       "151773      ZWE    Africa     Zimbabwe 2021-12-31     1530.0         7.0   \n",
       "151774      ZWE    Africa     Zimbabwe 2022-01-01      956.0        13.0   \n",
       "151775      ZWE    Africa     Zimbabwe 2022-01-02        NaN         NaN   \n",
       "\n",
       "        new_cases_smoothed_per_million  reproduction_rate  new_tests_smoothed  \\\n",
       "0                                  NaN                NaN                 NaN   \n",
       "1                                  NaN                NaN                 NaN   \n",
       "2                                  NaN                NaN                 NaN   \n",
       "3                                  NaN                NaN                 NaN   \n",
       "4                                  NaN                NaN                 NaN   \n",
       "...                                ...                ...                 ...   \n",
       "151771                          77.088                NaN              4563.0   \n",
       "151772                          98.291                NaN              5017.0   \n",
       "151773                          99.598                NaN              5259.0   \n",
       "151774                          99.086                NaN              5358.0   \n",
       "151775                             NaN                NaN              5502.0   \n",
       "\n",
       "            tests_units  ...  aged_70_older  gdp_per_capita  extreme_poverty  \\\n",
       "0                   NaN  ...          1.337        1803.987              NaN   \n",
       "1                   NaN  ...          1.337        1803.987              NaN   \n",
       "2                   NaN  ...          1.337        1803.987              NaN   \n",
       "3                   NaN  ...          1.337        1803.987              NaN   \n",
       "4                   NaN  ...          1.337        1803.987              NaN   \n",
       "...                 ...  ...            ...             ...              ...   \n",
       "151771  tests performed  ...          1.882        1899.775             21.4   \n",
       "151772  tests performed  ...          1.882        1899.775             21.4   \n",
       "151773  tests performed  ...          1.882        1899.775             21.4   \n",
       "151774  tests performed  ...          1.882        1899.775             21.4   \n",
       "151775  tests performed  ...          1.882        1899.775             21.4   \n",
       "\n",
       "        cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "0                     597.029                 9.59             NaN   \n",
       "1                     597.029                 9.59             NaN   \n",
       "2                     597.029                 9.59             NaN   \n",
       "3                     597.029                 9.59             NaN   \n",
       "4                     597.029                 9.59             NaN   \n",
       "...                       ...                  ...             ...   \n",
       "151771                307.846                 1.82             1.6   \n",
       "151772                307.846                 1.82             1.6   \n",
       "151773                307.846                 1.82             1.6   \n",
       "151774                307.846                 1.82             1.6   \n",
       "151775                307.846                 1.82             1.6   \n",
       "\n",
       "        male_smokers  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                NaN                         0.5            64.83   \n",
       "1                NaN                         0.5            64.83   \n",
       "2                NaN                         0.5            64.83   \n",
       "3                NaN                         0.5            64.83   \n",
       "4                NaN                         0.5            64.83   \n",
       "...              ...                         ...              ...   \n",
       "151771          30.7                         1.7            61.49   \n",
       "151772          30.7                         1.7            61.49   \n",
       "151773          30.7                         1.7            61.49   \n",
       "151774          30.7                         1.7            61.49   \n",
       "151775          30.7                         1.7            61.49   \n",
       "\n",
       "        human_development_index  \n",
       "0                         0.511  \n",
       "1                         0.511  \n",
       "2                         0.511  \n",
       "3                         0.511  \n",
       "4                         0.511  \n",
       "...                         ...  \n",
       "151771                    0.571  \n",
       "151772                    0.571  \n",
       "151773                    0.571  \n",
       "151774                    0.571  \n",
       "151775                    0.571  \n",
       "\n",
       "[141649 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping 'smoothed' columns\n",
    "dropped_smooth_df=dropped_nulls_df.drop(['new_cases_smoothed','new_deaths_smoothed','new_deaths_smoothed_per_million','new_tests_smoothed_per_thousand','new_deaths_per_million','new_cases_per_million','total_cases_per_million'], axis=1)\n",
    "dropped_smooth_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95c6454c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                              0\n",
       "continent                             0\n",
       "location                              0\n",
       "date                                  0\n",
       "new_cases                          2427\n",
       "new_deaths                        19298\n",
       "new_cases_smoothed_per_million     3496\n",
       "reproduction_rate                 29315\n",
       "new_tests_smoothed                68872\n",
       "tests_units                       66719\n",
       "stringency_index                  23967\n",
       "population                            0\n",
       "population_density                 7902\n",
       "median_age                        16267\n",
       "aged_65_older                     17637\n",
       "aged_70_older                     16944\n",
       "gdp_per_capita                    16520\n",
       "extreme_poverty                   59513\n",
       "cardiovasc_death_rate             17258\n",
       "diabetes_prevalence               10847\n",
       "female_smokers                    45460\n",
       "male_smokers                      46856\n",
       "hospital_beds_per_thousand        29334\n",
       "life_expectancy                     698\n",
       "human_development_index           17783\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_smooth_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62a39d3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 141649 entries, 0 to 151775\n",
      "Data columns (total 25 columns):\n",
      " #   Column                          Non-Null Count   Dtype         \n",
      "---  ------                          --------------   -----         \n",
      " 0   iso_code                        141649 non-null  object        \n",
      " 1   continent                       141649 non-null  object        \n",
      " 2   location                        141649 non-null  object        \n",
      " 3   date                            141649 non-null  datetime64[ns]\n",
      " 4   new_cases                       139222 non-null  float64       \n",
      " 5   new_deaths                      122351 non-null  float64       \n",
      " 6   new_cases_smoothed_per_million  138153 non-null  float64       \n",
      " 7   reproduction_rate               112334 non-null  float64       \n",
      " 8   new_tests_smoothed              72777 non-null   float64       \n",
      " 9   tests_units                     74930 non-null   object        \n",
      " 10  stringency_index                117682 non-null  float64       \n",
      " 11  population                      141649 non-null  float64       \n",
      " 12  population_density              133747 non-null  float64       \n",
      " 13  median_age                      125382 non-null  float64       \n",
      " 14  aged_65_older                   124012 non-null  float64       \n",
      " 15  aged_70_older                   124705 non-null  float64       \n",
      " 16  gdp_per_capita                  125129 non-null  float64       \n",
      " 17  extreme_poverty                 82136 non-null   float64       \n",
      " 18  cardiovasc_death_rate           124391 non-null  float64       \n",
      " 19  diabetes_prevalence             130802 non-null  float64       \n",
      " 20  female_smokers                  96189 non-null   float64       \n",
      " 21  male_smokers                    94793 non-null   float64       \n",
      " 22  hospital_beds_per_thousand      112315 non-null  float64       \n",
      " 23  life_expectancy                 140951 non-null  float64       \n",
      " 24  human_development_index         123866 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(20), object(4)\n",
      "memory usage: 28.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dropped_smooth_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21415b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>tests_units</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.381</td>\n",
       "      <td>1.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.326</td>\n",
       "      <td>1.18</td>\n",
       "      <td>37.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.470</td>\n",
       "      <td>1.14</td>\n",
       "      <td>47.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.768</td>\n",
       "      <td>1.08</td>\n",
       "      <td>56.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.017</td>\n",
       "      <td>1.05</td>\n",
       "      <td>61.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151758</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>248.379</td>\n",
       "      <td>1.42</td>\n",
       "      <td>12471.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151759</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>7510.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>319.466</td>\n",
       "      <td>1.35</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151760</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>232.524</td>\n",
       "      <td>1.23</td>\n",
       "      <td>11251.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151761</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-19</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>232.221</td>\n",
       "      <td>1.15</td>\n",
       "      <td>10478.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151762</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>264.461</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40877 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent  location       date  new_cases  new_deaths  \\\n",
       "1397        ALB    Europe   Albania 2020-03-24       19.0         1.0   \n",
       "1398        ALB    Europe   Albania 2020-03-25       23.0         0.0   \n",
       "1399        ALB    Europe   Albania 2020-03-26       28.0         1.0   \n",
       "1400        ALB    Europe   Albania 2020-03-27       12.0         2.0   \n",
       "1401        ALB    Europe   Albania 2020-03-28       11.0         2.0   \n",
       "...         ...       ...       ...        ...        ...         ...   \n",
       "151758      ZWE    Africa  Zimbabwe 2021-12-16     4367.0        14.0   \n",
       "151759      ZWE    Africa  Zimbabwe 2021-12-17     7510.0        20.0   \n",
       "151760      ZWE    Africa  Zimbabwe 2021-12-18        0.0         0.0   \n",
       "151761      ZWE    Africa  Zimbabwe 2021-12-19     2106.0         3.0   \n",
       "151762      ZWE    Africa  Zimbabwe 2021-12-20     3406.0        23.0   \n",
       "\n",
       "        new_cases_smoothed_per_million  reproduction_rate  new_tests_smoothed  \\\n",
       "1397                             3.381               1.20                40.0   \n",
       "1398                             4.326               1.18                37.0   \n",
       "1399                             5.470               1.14                47.0   \n",
       "1400                             5.768               1.08                56.0   \n",
       "1401                             6.017               1.05                61.0   \n",
       "...                                ...                ...                 ...   \n",
       "151758                         248.379               1.42             12471.0   \n",
       "151759                         319.466               1.35             11700.0   \n",
       "151760                         232.524               1.23             11251.0   \n",
       "151761                         232.221               1.15             10478.0   \n",
       "151762                         264.461               1.08              9270.0   \n",
       "\n",
       "            tests_units  ...  aged_70_older  gdp_per_capita  extreme_poverty  \\\n",
       "1397    tests performed  ...          8.643       11803.431              1.1   \n",
       "1398    tests performed  ...          8.643       11803.431              1.1   \n",
       "1399    tests performed  ...          8.643       11803.431              1.1   \n",
       "1400    tests performed  ...          8.643       11803.431              1.1   \n",
       "1401    tests performed  ...          8.643       11803.431              1.1   \n",
       "...                 ...  ...            ...             ...              ...   \n",
       "151758  tests performed  ...          1.882        1899.775             21.4   \n",
       "151759  tests performed  ...          1.882        1899.775             21.4   \n",
       "151760  tests performed  ...          1.882        1899.775             21.4   \n",
       "151761  tests performed  ...          1.882        1899.775             21.4   \n",
       "151762  tests performed  ...          1.882        1899.775             21.4   \n",
       "\n",
       "        cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "1397                  304.195                10.08             7.1   \n",
       "1398                  304.195                10.08             7.1   \n",
       "1399                  304.195                10.08             7.1   \n",
       "1400                  304.195                10.08             7.1   \n",
       "1401                  304.195                10.08             7.1   \n",
       "...                       ...                  ...             ...   \n",
       "151758                307.846                 1.82             1.6   \n",
       "151759                307.846                 1.82             1.6   \n",
       "151760                307.846                 1.82             1.6   \n",
       "151761                307.846                 1.82             1.6   \n",
       "151762                307.846                 1.82             1.6   \n",
       "\n",
       "        male_smokers  hospital_beds_per_thousand  life_expectancy  \\\n",
       "1397            51.2                        2.89            78.57   \n",
       "1398            51.2                        2.89            78.57   \n",
       "1399            51.2                        2.89            78.57   \n",
       "1400            51.2                        2.89            78.57   \n",
       "1401            51.2                        2.89            78.57   \n",
       "...              ...                         ...              ...   \n",
       "151758          30.7                        1.70            61.49   \n",
       "151759          30.7                        1.70            61.49   \n",
       "151760          30.7                        1.70            61.49   \n",
       "151761          30.7                        1.70            61.49   \n",
       "151762          30.7                        1.70            61.49   \n",
       "\n",
       "        human_development_index  \n",
       "1397                      0.795  \n",
       "1398                      0.795  \n",
       "1399                      0.795  \n",
       "1400                      0.795  \n",
       "1401                      0.795  \n",
       "...                         ...  \n",
       "151758                    0.571  \n",
       "151759                    0.571  \n",
       "151760                    0.571  \n",
       "151761                    0.571  \n",
       "151762                    0.571  \n",
       "\n",
       "[40877 rows x 25 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping 'na'\n",
    "cleaned_df=dropped_smooth_df.dropna()\n",
    "cleaned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6bfbb10",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df=cleaned_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ee6a2d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Italy       666\n",
       "Belgium     657\n",
       "Denmark     653\n",
       "Portugal    651\n",
       "Malaysia    649\n",
       "           ... \n",
       "Moldova     173\n",
       "Laos        160\n",
       "Gambia      135\n",
       "Timor        92\n",
       "Fiji         83\n",
       "Name: location, Length: 75, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c6ce61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>37.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>47.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>61.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40872</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>12471.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40873</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>7510.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40874</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>11251.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40875</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-19</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>10478.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40876</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40877 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent  location       date  new_cases  new_deaths  \\\n",
       "0        Europe   Albania 2020-03-24       19.0         1.0   \n",
       "1        Europe   Albania 2020-03-25       23.0         0.0   \n",
       "2        Europe   Albania 2020-03-26       28.0         1.0   \n",
       "3        Europe   Albania 2020-03-27       12.0         2.0   \n",
       "4        Europe   Albania 2020-03-28       11.0         2.0   \n",
       "...         ...       ...        ...        ...         ...   \n",
       "40872    Africa  Zimbabwe 2021-12-16     4367.0        14.0   \n",
       "40873    Africa  Zimbabwe 2021-12-17     7510.0        20.0   \n",
       "40874    Africa  Zimbabwe 2021-12-18        0.0         0.0   \n",
       "40875    Africa  Zimbabwe 2021-12-19     2106.0         3.0   \n",
       "40876    Africa  Zimbabwe 2021-12-20     3406.0        23.0   \n",
       "\n",
       "       reproduction_rate  new_tests_smoothed  stringency_index  population  \\\n",
       "0                   1.20                40.0             84.26   2872934.0   \n",
       "1                   1.18                37.0             84.26   2872934.0   \n",
       "2                   1.14                47.0             84.26   2872934.0   \n",
       "3                   1.08                56.0             84.26   2872934.0   \n",
       "4                   1.05                61.0             84.26   2872934.0   \n",
       "...                  ...                 ...               ...         ...   \n",
       "40872               1.42             12471.0             63.89  15092171.0   \n",
       "40873               1.35             11700.0             63.89  15092171.0   \n",
       "40874               1.23             11251.0             63.89  15092171.0   \n",
       "40875               1.15             10478.0             63.89  15092171.0   \n",
       "40876               1.08              9270.0             63.89  15092171.0   \n",
       "\n",
       "       population_density  ...  aged_70_older  gdp_per_capita  \\\n",
       "0                 104.871  ...          8.643       11803.431   \n",
       "1                 104.871  ...          8.643       11803.431   \n",
       "2                 104.871  ...          8.643       11803.431   \n",
       "3                 104.871  ...          8.643       11803.431   \n",
       "4                 104.871  ...          8.643       11803.431   \n",
       "...                   ...  ...            ...             ...   \n",
       "40872              42.729  ...          1.882        1899.775   \n",
       "40873              42.729  ...          1.882        1899.775   \n",
       "40874              42.729  ...          1.882        1899.775   \n",
       "40875              42.729  ...          1.882        1899.775   \n",
       "40876              42.729  ...          1.882        1899.775   \n",
       "\n",
       "       extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0                  1.1                304.195                10.08   \n",
       "1                  1.1                304.195                10.08   \n",
       "2                  1.1                304.195                10.08   \n",
       "3                  1.1                304.195                10.08   \n",
       "4                  1.1                304.195                10.08   \n",
       "...                ...                    ...                  ...   \n",
       "40872             21.4                307.846                 1.82   \n",
       "40873             21.4                307.846                 1.82   \n",
       "40874             21.4                307.846                 1.82   \n",
       "40875             21.4                307.846                 1.82   \n",
       "40876             21.4                307.846                 1.82   \n",
       "\n",
       "       female_smokers  male_smokers  hospital_beds_per_thousand  \\\n",
       "0                 7.1          51.2                        2.89   \n",
       "1                 7.1          51.2                        2.89   \n",
       "2                 7.1          51.2                        2.89   \n",
       "3                 7.1          51.2                        2.89   \n",
       "4                 7.1          51.2                        2.89   \n",
       "...               ...           ...                         ...   \n",
       "40872             1.6          30.7                        1.70   \n",
       "40873             1.6          30.7                        1.70   \n",
       "40874             1.6          30.7                        1.70   \n",
       "40875             1.6          30.7                        1.70   \n",
       "40876             1.6          30.7                        1.70   \n",
       "\n",
       "       life_expectancy  human_development_index  \n",
       "0                78.57                    0.795  \n",
       "1                78.57                    0.795  \n",
       "2                78.57                    0.795  \n",
       "3                78.57                    0.795  \n",
       "4                78.57                    0.795  \n",
       "...                ...                      ...  \n",
       "40872            61.49                    0.571  \n",
       "40873            61.49                    0.571  \n",
       "40874            61.49                    0.571  \n",
       "40875            61.49                    0.571  \n",
       "40876            61.49                    0.571  \n",
       "\n",
       "[40877 rows x 22 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data preprocessing - dropping 'iso_code' as it is redundant; location has the same purpose - also removing 'new_cases_smoothed_per_million' + 'tests_units'\n",
    "cleaned_df = cleaned_df.drop(['iso_code', 'new_cases_smoothed_per_million', 'tests_units'],axis=1)\n",
    "cleaned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9630e91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40877 entries, 0 to 40876\n",
      "Data columns (total 22 columns):\n",
      " #   Column                      Non-Null Count  Dtype         \n",
      "---  ------                      --------------  -----         \n",
      " 0   continent                   40877 non-null  object        \n",
      " 1   location                    40877 non-null  object        \n",
      " 2   date                        40877 non-null  datetime64[ns]\n",
      " 3   new_cases                   40877 non-null  float64       \n",
      " 4   new_deaths                  40877 non-null  float64       \n",
      " 5   reproduction_rate           40877 non-null  float64       \n",
      " 6   new_tests_smoothed          40877 non-null  float64       \n",
      " 7   stringency_index            40877 non-null  float64       \n",
      " 8   population                  40877 non-null  float64       \n",
      " 9   population_density          40877 non-null  float64       \n",
      " 10  median_age                  40877 non-null  float64       \n",
      " 11  aged_65_older               40877 non-null  float64       \n",
      " 12  aged_70_older               40877 non-null  float64       \n",
      " 13  gdp_per_capita              40877 non-null  float64       \n",
      " 14  extreme_poverty             40877 non-null  float64       \n",
      " 15  cardiovasc_death_rate       40877 non-null  float64       \n",
      " 16  diabetes_prevalence         40877 non-null  float64       \n",
      " 17  female_smokers              40877 non-null  float64       \n",
      " 18  male_smokers                40877 non-null  float64       \n",
      " 19  hospital_beds_per_thousand  40877 non-null  float64       \n",
      " 20  life_expectancy             40877 non-null  float64       \n",
      " 21  human_development_index     40877 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(19), object(2)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "cleaned_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f5ce336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# output_data_file = \"Covid_Cleaned_Database.csv\"\n",
    "# cleaned_df.to_csv(output_data_file, index_label=\"Row_ID\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee93dd8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column continent has 0 null values\n",
      "Column location has 0 null values\n",
      "Column date has 0 null values\n",
      "Column new_cases has 0 null values\n",
      "Column new_deaths has 0 null values\n",
      "Column reproduction_rate has 0 null values\n",
      "Column new_tests_smoothed has 0 null values\n",
      "Column stringency_index has 0 null values\n",
      "Column population has 0 null values\n",
      "Column population_density has 0 null values\n",
      "Column median_age has 0 null values\n",
      "Column aged_65_older has 0 null values\n",
      "Column aged_70_older has 0 null values\n",
      "Column gdp_per_capita has 0 null values\n",
      "Column extreme_poverty has 0 null values\n",
      "Column cardiovasc_death_rate has 0 null values\n",
      "Column diabetes_prevalence has 0 null values\n",
      "Column female_smokers has 0 null values\n",
      "Column male_smokers has 0 null values\n",
      "Column hospital_beds_per_thousand has 0 null values\n",
      "Column life_expectancy has 0 null values\n",
      "Column human_development_index has 0 null values\n"
     ]
    }
   ],
   "source": [
    "#checking nulls\n",
    "for column in cleaned_df.columns:\n",
    "    print(f\"Column {column} has {cleaned_df[column].isnull().sum()} null values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ece5455",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'config'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_387279/1407237638.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdb_password\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfile_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Resources'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'config'"
     ]
    }
   ],
   "source": [
    "from config import db_password\n",
    "file_dir = 'Resources'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a8cdd9",
   "metadata": {},
   "source": [
    "## Creating Target and Feature Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccabb98c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>37.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>47.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>61.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40872</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>12471.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40873</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>7510.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40874</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>11251.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40875</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-19</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>10478.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40876</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40877 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent  location       date  new_cases  reproduction_rate  \\\n",
       "0        Europe   Albania 2020-03-24       19.0               1.20   \n",
       "1        Europe   Albania 2020-03-25       23.0               1.18   \n",
       "2        Europe   Albania 2020-03-26       28.0               1.14   \n",
       "3        Europe   Albania 2020-03-27       12.0               1.08   \n",
       "4        Europe   Albania 2020-03-28       11.0               1.05   \n",
       "...         ...       ...        ...        ...                ...   \n",
       "40872    Africa  Zimbabwe 2021-12-16     4367.0               1.42   \n",
       "40873    Africa  Zimbabwe 2021-12-17     7510.0               1.35   \n",
       "40874    Africa  Zimbabwe 2021-12-18        0.0               1.23   \n",
       "40875    Africa  Zimbabwe 2021-12-19     2106.0               1.15   \n",
       "40876    Africa  Zimbabwe 2021-12-20     3406.0               1.08   \n",
       "\n",
       "       new_tests_smoothed  stringency_index  population  population_density  \\\n",
       "0                    40.0             84.26   2872934.0             104.871   \n",
       "1                    37.0             84.26   2872934.0             104.871   \n",
       "2                    47.0             84.26   2872934.0             104.871   \n",
       "3                    56.0             84.26   2872934.0             104.871   \n",
       "4                    61.0             84.26   2872934.0             104.871   \n",
       "...                   ...               ...         ...                 ...   \n",
       "40872             12471.0             63.89  15092171.0              42.729   \n",
       "40873             11700.0             63.89  15092171.0              42.729   \n",
       "40874             11251.0             63.89  15092171.0              42.729   \n",
       "40875             10478.0             63.89  15092171.0              42.729   \n",
       "40876              9270.0             63.89  15092171.0              42.729   \n",
       "\n",
       "       median_age  ...  aged_70_older  gdp_per_capita  extreme_poverty  \\\n",
       "0            38.0  ...          8.643       11803.431              1.1   \n",
       "1            38.0  ...          8.643       11803.431              1.1   \n",
       "2            38.0  ...          8.643       11803.431              1.1   \n",
       "3            38.0  ...          8.643       11803.431              1.1   \n",
       "4            38.0  ...          8.643       11803.431              1.1   \n",
       "...           ...  ...            ...             ...              ...   \n",
       "40872        19.6  ...          1.882        1899.775             21.4   \n",
       "40873        19.6  ...          1.882        1899.775             21.4   \n",
       "40874        19.6  ...          1.882        1899.775             21.4   \n",
       "40875        19.6  ...          1.882        1899.775             21.4   \n",
       "40876        19.6  ...          1.882        1899.775             21.4   \n",
       "\n",
       "       cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "0                    304.195                10.08             7.1   \n",
       "1                    304.195                10.08             7.1   \n",
       "2                    304.195                10.08             7.1   \n",
       "3                    304.195                10.08             7.1   \n",
       "4                    304.195                10.08             7.1   \n",
       "...                      ...                  ...             ...   \n",
       "40872                307.846                 1.82             1.6   \n",
       "40873                307.846                 1.82             1.6   \n",
       "40874                307.846                 1.82             1.6   \n",
       "40875                307.846                 1.82             1.6   \n",
       "40876                307.846                 1.82             1.6   \n",
       "\n",
       "       male_smokers  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0              51.2                        2.89            78.57   \n",
       "1              51.2                        2.89            78.57   \n",
       "2              51.2                        2.89            78.57   \n",
       "3              51.2                        2.89            78.57   \n",
       "4              51.2                        2.89            78.57   \n",
       "...             ...                         ...              ...   \n",
       "40872          30.7                        1.70            61.49   \n",
       "40873          30.7                        1.70            61.49   \n",
       "40874          30.7                        1.70            61.49   \n",
       "40875          30.7                        1.70            61.49   \n",
       "40876          30.7                        1.70            61.49   \n",
       "\n",
       "       human_development_index  \n",
       "0                        0.795  \n",
       "1                        0.795  \n",
       "2                        0.795  \n",
       "3                        0.795  \n",
       "4                        0.795  \n",
       "...                        ...  \n",
       "40872                    0.571  \n",
       "40873                    0.571  \n",
       "40874                    0.571  \n",
       "40875                    0.571  \n",
       "40876                    0.571  \n",
       "\n",
       "[40877 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating X as feature variables, excluding the target variable\n",
    "X=cleaned_df.drop(['new_deaths'],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ac4ff8c6-9f2a-4dac-9946-1f67c07aba7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40872</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40873</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40874</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40875</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40876</th>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40877 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       new_deaths\n",
       "0             1.0\n",
       "1             0.0\n",
       "2             1.0\n",
       "3             2.0\n",
       "4             2.0\n",
       "...           ...\n",
       "40872        14.0\n",
       "40873        20.0\n",
       "40874         0.0\n",
       "40875         3.0\n",
       "40876        23.0\n",
       "\n",
       "[40877 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating target variable of \"new deaths\" as y\n",
    "y=cleaned_df['new_deaths']\n",
    "y=pd.DataFrame(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6a3629c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sqlalchemy import create_engine\n",
    "# db_string = f\"postgresql://postgres:{db_password}@127.0.0.1:5432/Covid_data\"\n",
    "# engine = create_engine(db_string)\n",
    "# X.to_sql('features', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cbdbe95e-2c00-4374-884d-a6f29cc78439",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'db_password' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_387279/213766221.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msqlalchemy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msqlalchemy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcreate_engine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdb_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"postgresql://postgres:{db_password}@127.0.0.1:5432/Covid_data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdb_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0minsp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqlalchemy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'db_password' is not defined"
     ]
    }
   ],
   "source": [
    "# Import SqlAlchemy and establish connection to the database\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func\n",
    "db_string = f\"postgresql://postgres:{db_password}@127.0.0.1:5432/Covid_data\"\n",
    "engine = create_engine(db_string)\n",
    "insp = sqlalchemy.inspect(engine)\n",
    "db_list = insp.get_schema_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae1a75c-9a6e-4a12-855a-08a535c7c81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a connection to the SQL database\n",
    "conn = engine.connect()\n",
    "# Reflecting Features from Postgres\n",
    "X = pd.read_sql(\"SELECT * FROM features\", conn)\n",
    "X = X.drop(['index'],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "779c07b5-a6b0-463b-abf1-273723a0868b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'engine' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_387279/4112854272.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Make a connection to the SQL database\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# Reflecting Target Variable from Postgres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT * FROM target_variable\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'engine' is not defined"
     ]
    }
   ],
   "source": [
    "# Make a connection to the SQL database\n",
    "conn = engine.connect()\n",
    "# Reflecting Target Variable from Postgres\n",
    "y = pd.read_sql(\"SELECT * FROM target_variable\", conn)\n",
    "y = y.drop(['index'],axis=1)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c2e1e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing dependancies\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f9b41040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>reproduction_rate</th>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>37.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>47.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>61.0</td>\n",
       "      <td>84.26</td>\n",
       "      <td>2872934.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.643</td>\n",
       "      <td>11803.431</td>\n",
       "      <td>1.1</td>\n",
       "      <td>304.195</td>\n",
       "      <td>10.08</td>\n",
       "      <td>7.1</td>\n",
       "      <td>51.2</td>\n",
       "      <td>2.89</td>\n",
       "      <td>78.57</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40872</th>\n",
       "      <td>Africa</td>\n",
       "      <td>74</td>\n",
       "      <td>664</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>1.42</td>\n",
       "      <td>12471.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40873</th>\n",
       "      <td>Africa</td>\n",
       "      <td>74</td>\n",
       "      <td>665</td>\n",
       "      <td>7510.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40874</th>\n",
       "      <td>Africa</td>\n",
       "      <td>74</td>\n",
       "      <td>666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>11251.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40875</th>\n",
       "      <td>Africa</td>\n",
       "      <td>74</td>\n",
       "      <td>667</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>10478.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40876</th>\n",
       "      <td>Africa</td>\n",
       "      <td>74</td>\n",
       "      <td>668</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>63.89</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1.70</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40877 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent  location  date  new_cases  reproduction_rate  \\\n",
       "0        Europe         0    32       19.0               1.20   \n",
       "1        Europe         0    33       23.0               1.18   \n",
       "2        Europe         0    34       28.0               1.14   \n",
       "3        Europe         0    35       12.0               1.08   \n",
       "4        Europe         0    36       11.0               1.05   \n",
       "...         ...       ...   ...        ...                ...   \n",
       "40872    Africa        74   664     4367.0               1.42   \n",
       "40873    Africa        74   665     7510.0               1.35   \n",
       "40874    Africa        74   666        0.0               1.23   \n",
       "40875    Africa        74   667     2106.0               1.15   \n",
       "40876    Africa        74   668     3406.0               1.08   \n",
       "\n",
       "       new_tests_smoothed  stringency_index  population  population_density  \\\n",
       "0                    40.0             84.26   2872934.0             104.871   \n",
       "1                    37.0             84.26   2872934.0             104.871   \n",
       "2                    47.0             84.26   2872934.0             104.871   \n",
       "3                    56.0             84.26   2872934.0             104.871   \n",
       "4                    61.0             84.26   2872934.0             104.871   \n",
       "...                   ...               ...         ...                 ...   \n",
       "40872             12471.0             63.89  15092171.0              42.729   \n",
       "40873             11700.0             63.89  15092171.0              42.729   \n",
       "40874             11251.0             63.89  15092171.0              42.729   \n",
       "40875             10478.0             63.89  15092171.0              42.729   \n",
       "40876              9270.0             63.89  15092171.0              42.729   \n",
       "\n",
       "       median_age  ...  aged_70_older  gdp_per_capita  extreme_poverty  \\\n",
       "0            38.0  ...          8.643       11803.431              1.1   \n",
       "1            38.0  ...          8.643       11803.431              1.1   \n",
       "2            38.0  ...          8.643       11803.431              1.1   \n",
       "3            38.0  ...          8.643       11803.431              1.1   \n",
       "4            38.0  ...          8.643       11803.431              1.1   \n",
       "...           ...  ...            ...             ...              ...   \n",
       "40872        19.6  ...          1.882        1899.775             21.4   \n",
       "40873        19.6  ...          1.882        1899.775             21.4   \n",
       "40874        19.6  ...          1.882        1899.775             21.4   \n",
       "40875        19.6  ...          1.882        1899.775             21.4   \n",
       "40876        19.6  ...          1.882        1899.775             21.4   \n",
       "\n",
       "       cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "0                    304.195                10.08             7.1   \n",
       "1                    304.195                10.08             7.1   \n",
       "2                    304.195                10.08             7.1   \n",
       "3                    304.195                10.08             7.1   \n",
       "4                    304.195                10.08             7.1   \n",
       "...                      ...                  ...             ...   \n",
       "40872                307.846                 1.82             1.6   \n",
       "40873                307.846                 1.82             1.6   \n",
       "40874                307.846                 1.82             1.6   \n",
       "40875                307.846                 1.82             1.6   \n",
       "40876                307.846                 1.82             1.6   \n",
       "\n",
       "       male_smokers  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0              51.2                        2.89            78.57   \n",
       "1              51.2                        2.89            78.57   \n",
       "2              51.2                        2.89            78.57   \n",
       "3              51.2                        2.89            78.57   \n",
       "4              51.2                        2.89            78.57   \n",
       "...             ...                         ...              ...   \n",
       "40872          30.7                        1.70            61.49   \n",
       "40873          30.7                        1.70            61.49   \n",
       "40874          30.7                        1.70            61.49   \n",
       "40875          30.7                        1.70            61.49   \n",
       "40876          30.7                        1.70            61.49   \n",
       "\n",
       "       human_development_index  \n",
       "0                        0.795  \n",
       "1                        0.795  \n",
       "2                        0.795  \n",
       "3                        0.795  \n",
       "4                        0.795  \n",
       "...                        ...  \n",
       "40872                    0.571  \n",
       "40873                    0.571  \n",
       "40874                    0.571  \n",
       "40875                    0.571  \n",
       "40876                    0.571  \n",
       "\n",
       "[40877 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['location'] = LabelEncoder().fit_transform(X['location'])\n",
    "X['date'] = LabelEncoder().fit_transform(X['date'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0116bddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40877 entries, 0 to 40876\n",
      "Data columns (total 21 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   continent                   40877 non-null  object \n",
      " 1   location                    40877 non-null  int64  \n",
      " 2   date                        40877 non-null  int64  \n",
      " 3   new_cases                   40877 non-null  float64\n",
      " 4   reproduction_rate           40877 non-null  float64\n",
      " 5   new_tests_smoothed          40877 non-null  float64\n",
      " 6   stringency_index            40877 non-null  float64\n",
      " 7   population                  40877 non-null  float64\n",
      " 8   population_density          40877 non-null  float64\n",
      " 9   median_age                  40877 non-null  float64\n",
      " 10  aged_65_older               40877 non-null  float64\n",
      " 11  aged_70_older               40877 non-null  float64\n",
      " 12  gdp_per_capita              40877 non-null  float64\n",
      " 13  extreme_poverty             40877 non-null  float64\n",
      " 14  cardiovasc_death_rate       40877 non-null  float64\n",
      " 15  diabetes_prevalence         40877 non-null  float64\n",
      " 16  female_smokers              40877 non-null  float64\n",
      " 17  male_smokers                40877 non-null  float64\n",
      " 18  hospital_beds_per_thousand  40877 non-null  float64\n",
      " 19  life_expectancy             40877 non-null  float64\n",
      " 20  human_development_index     40877 non-null  float64\n",
      "dtypes: float64(18), int64(2), object(1)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1387a40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['continent']"
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "continent_cat = X.dtypes[X.dtypes == \"object\"].index.tolist()\n",
    "continent_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "089655bc",
   "metadata": {},
   "outputs": [
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       "         new_deaths\n",
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       "std      313.729639\n",
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    "y.describe()"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4bbbd7ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/wernerm/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
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       "   continent_Africa  continent_Asia  continent_Europe  \\\n",
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       "2               0.0             0.0               1.0   \n",
       "3               0.0             0.0               1.0   \n",
       "4               0.0             0.0               1.0   \n",
       "\n",
       "   continent_North America  continent_Oceania  continent_South America  \n",
       "0                      0.0                0.0                      0.0  \n",
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       "2                      0.0                0.0                      0.0  \n",
       "3                      0.0                0.0                      0.0  \n",
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     "execution_count": 38,
     "metadata": {},
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    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(X[continent_cat]))\n",
    "\n",
    "# Add the encoded variable names to the dataframe\n",
    "encode_df.columns = enc.get_feature_names(continent_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba8c93f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_387279/1881480399.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  X = X.drop(continent_cat,1)\n"
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       "<p>40877 rows × 26 columns</p>\n",
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      ],
      "text/plain": [
       "       location  date  new_cases  reproduction_rate  new_tests_smoothed  \\\n",
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       "1             0    33       23.0               1.18                37.0   \n",
       "2             0    34       28.0               1.14                47.0   \n",
       "3             0    35       12.0               1.08                56.0   \n",
       "4             0    36       11.0               1.05                61.0   \n",
       "...         ...   ...        ...                ...                 ...   \n",
       "40872        74   664     4367.0               1.42             12471.0   \n",
       "40873        74   665     7510.0               1.35             11700.0   \n",
       "40874        74   666        0.0               1.23             11251.0   \n",
       "40875        74   667     2106.0               1.15             10478.0   \n",
       "40876        74   668     3406.0               1.08              9270.0   \n",
       "\n",
       "       stringency_index  population  population_density  median_age  \\\n",
       "0                 84.26   2872934.0             104.871        38.0   \n",
       "1                 84.26   2872934.0             104.871        38.0   \n",
       "2                 84.26   2872934.0             104.871        38.0   \n",
       "3                 84.26   2872934.0             104.871        38.0   \n",
       "4                 84.26   2872934.0             104.871        38.0   \n",
       "...                 ...         ...                 ...         ...   \n",
       "40872             63.89  15092171.0              42.729        19.6   \n",
       "40873             63.89  15092171.0              42.729        19.6   \n",
       "40874             63.89  15092171.0              42.729        19.6   \n",
       "40875             63.89  15092171.0              42.729        19.6   \n",
       "40876             63.89  15092171.0              42.729        19.6   \n",
       "\n",
       "       aged_65_older  ...  male_smokers  hospital_beds_per_thousand  \\\n",
       "0             13.188  ...          51.2                        2.89   \n",
       "1             13.188  ...          51.2                        2.89   \n",
       "2             13.188  ...          51.2                        2.89   \n",
       "3             13.188  ...          51.2                        2.89   \n",
       "4             13.188  ...          51.2                        2.89   \n",
       "...              ...  ...           ...                         ...   \n",
       "40872          2.822  ...          30.7                        1.70   \n",
       "40873          2.822  ...          30.7                        1.70   \n",
       "40874          2.822  ...          30.7                        1.70   \n",
       "40875          2.822  ...          30.7                        1.70   \n",
       "40876          2.822  ...          30.7                        1.70   \n",
       "\n",
       "       life_expectancy  human_development_index  continent_Africa  \\\n",
       "0                78.57                    0.795               0.0   \n",
       "1                78.57                    0.795               0.0   \n",
       "2                78.57                    0.795               0.0   \n",
       "3                78.57                    0.795               0.0   \n",
       "4                78.57                    0.795               0.0   \n",
       "...                ...                      ...               ...   \n",
       "40872            61.49                    0.571               1.0   \n",
       "40873            61.49                    0.571               1.0   \n",
       "40874            61.49                    0.571               1.0   \n",
       "40875            61.49                    0.571               1.0   \n",
       "40876            61.49                    0.571               1.0   \n",
       "\n",
       "       continent_Asia  continent_Europe  continent_North America  \\\n",
       "0                 0.0               1.0                      0.0   \n",
       "1                 0.0               1.0                      0.0   \n",
       "2                 0.0               1.0                      0.0   \n",
       "3                 0.0               1.0                      0.0   \n",
       "4                 0.0               1.0                      0.0   \n",
       "...               ...               ...                      ...   \n",
       "40872             0.0               0.0                      0.0   \n",
       "40873             0.0               0.0                      0.0   \n",
       "40874             0.0               0.0                      0.0   \n",
       "40875             0.0               0.0                      0.0   \n",
       "40876             0.0               0.0                      0.0   \n",
       "\n",
       "       continent_Oceania  continent_South America  \n",
       "0                    0.0                      0.0  \n",
       "1                    0.0                      0.0  \n",
       "2                    0.0                      0.0  \n",
       "3                    0.0                      0.0  \n",
       "4                    0.0                      0.0  \n",
       "...                  ...                      ...  \n",
       "40872                0.0                      0.0  \n",
       "40873                0.0                      0.0  \n",
       "40874                0.0                      0.0  \n",
       "40875                0.0                      0.0  \n",
       "40876                0.0                      0.0  \n",
       "\n",
       "[40877 rows x 26 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "X = X.merge(encode_df,left_index=True, right_index=True)\n",
    "X = X.drop(continent_cat,1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a2c6605f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#amending column names\n",
    "X = X.rename(columns = {\n",
    "    'continent_North America' : 'continent_North_America',\n",
    "    'continent_South America' : 'continent_South_America'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9425258",
   "metadata": {},
   "source": [
    "## Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ec22d3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the preprocessed data into a training and testing dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea31c1f",
   "metadata": {},
   "source": [
    "## Scaling for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ce11549a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ebaeed",
   "metadata": {},
   "source": [
    "## <font color='red'>Classification Model - Random Forest Classifier</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c66e2e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing dependancies\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "90e28730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9888116906416153\n",
      "Testing Score: 0.25459882583170257\n"
     ]
    }
   ],
   "source": [
    "#creating random forest classifier model\n",
    "clf = RandomForestClassifier(random_state=1, n_estimators=100, max_depth=20).fit(X_train_scaled, y_train.values.ravel())\n",
    "print(f'Training Score: {clf.score(X_train_scaled, y_train)}')\n",
    "print(f'Testing Score: {clf.score(X_test_scaled, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e2599258",
   "metadata": {},
   "outputs": [],
   "source": [
    "#identifying feature importances\n",
    "feature_importances = clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b6d5f59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIPoqZOX9mHvYFnrt/Zv7xzb2mm9Pha5yZJ8/Mzx3D9c6fmdfePKsDADi+ieijOz/JFxTRSfYnOa4jOifGGgEAjksnZERvd1HfPzMvmZn3zswbZuaOM3PfmXndzFw2MxfPzNkzc8rM/PHsuNvMfHZmHrFd5+KZ+bIjXT/J9yR55sxcMTMPn5l7zsxrZuZd29fXbmO/fhtzxcxcPjOnJTmQ5OHbvmfOzFfOzDu3n6+ambOO8rruPDMXzcyVM/OemXnctv+amfnXM/P2mTk4M189M6+fmf8wM9+zjZmZ+YntvKt3nXvE/Yevcdt37+39+9DM/PiudX3LNve7Z+bVM3OXbf+3zcwHZuZtSb7jBv69nr6t++D11117k/+dAQCOV/v2egHH4KwkT1hrPW1m/k2S70zylCTfs9b60Mx8TZIXr7W+cWY+mOR+Sb40yWXZicd3JPmStdYfHX7htdY1M/MLST6+1vrJJJmZX0vy02utt83MfZK8PslXJHlWku9da12yxeUnkzw7ybPWWv/jdu7/meRn1lqvnJk7JDnlKK/p25L8+Vrrgu2803cd+9O11kNn5qeTvDzJ1yb5oiTvTfIL2YnYc5Ock+QeSd41M2/Nzt30I+0/fI1P3sY9MMmnkvzhtu6/SfJ/JHnkWusTM/MvkvzgFtkvSfKNSf4oyauO8pqy1rowyYVJcuoZZ62jjQMAOFGcyBH94bXWFdv2Zdl5POFhSV49M4fGnLp9vzjJI7IT0S9I8rQkb0nyrmK+Rya5365r33W763xJkp+amVcm+c211p/tGnPI25M8Z2a+ZBvzoaPMcXWSn5yZFyZ57Vrr4l3HfnvXmLustT6W5GMz88mZuVuSr0vy62ut65P85cy8JcmDb2D/R48w/++tta5Nkpl5X5L/PsndsvMLyCXb67rD9nrOzs6/wYe28a9I8vSjvC4AgNuUE/Jxjs2ndm1fn+TuSf56rXXurq+v2I5fnOThSR6S5HeyE4bnJ3lrMd/tkjx017XPXGt9bK11IMl3J7ljkktn5uzDT1xr/VqSb8/OXd3Xz8w3HmmCtdYHkzwoO6H8gpn5kSO83s8e9to/m51fhv6bct8cbf+RHP6eHrruv9/1uu+31nrqoSUX1wYAuM04kSP6cB9N8uGZeWzyuWeBz9mOvSM7d6k/u9b6ZJIrkvzT7MT10XwsyWm7fn5Dkn926IeZOXf7ft+11tVrrRcmOZidO7Sfd+7M/A9J/nit9bPZuaP8gCNNODP3TnLdWusVSX4yyVffpFe+461JHrc9A37P7Nx5f+cN7D/89R3NpUm+9tCz4zNzp5n5e0k+kORLZ+a+27gnFGsFADih3ZYiOkmemOSpM3Nldp4VflSSrLU+leRPsxOEyU48n5adO75H8++SPObQBwuTfH+S87YPBr4vOx88TJJnbB/auzI7d5p/N8lVST6zfUDwmUkel+Q9M3NFdiL7V44y5/2TvHMb95wkP1a89t/a5r0yye8n+edrrf/3BvYfvsYjWmv9pyRPTvLrM3NVdt7Ds7dfRp6e5KLtg4V/UqwVAOCENmv5L/Lcek4946x1xpNetNfLAOAmuubABXu9BNgzM3PZWuu8Ix27rd2JBgCAW9yJ/Nc5bhYz85QkP3DY7kvWWt97C875xUl+7wiHvmmt9Z9vqXkBALh5nPQRvdZ6WZKX3cpz/ufs/E1mAABOQB7nAACAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgNK+vV4AJ5f7n3l6Dh64YK+XAQBwTNyJBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAIDSvr1eACeXqz9ybfY/+6K9XgbASemaAxfs9RLgNsOdaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloPmdmnjszz7qB44+emfvdmmsCADgeiWgaj04iogGAk56IPsnNzHNm5g9n5o1Jvnzb97SZedfMXDkzr5mZO83Mw5J8e5KfmJkrZua+29frZuaymbl4Zs7e0xcDAHArEdEnsZl5UJLHJ3lgku9I8uDt0G+utR681jonyfuTPHWt9QdJfjvJD621zl1r/YckFyb5vrXWg5I8K8mLb/UXAQCwB/bt9QLYUw9P8ltrreuSZGZ+e9v/VTPzY0nuluQuSV5/+Ikzc5ckD0vy6pk5tPvUI00yM09P8vQkOeWu97wZlw8AsDdENOsI+16e5NFrrStn5slJzj/CmNsl+eu11rk3OsFaF2bnrnVOPeOsI80HAHBC8TjHye2tSR4zM3ecmdOS/MNt/2lJ/mJmbp/kibvGf2w7lrXWR5N8eGYemySz45xbb+kAAHtHRJ/E1lrvTvKqJFckeU2Si7dDP5zkHUn+fZIP7Drl/07yQzNz+czcNzuB/dSZuTLJe5M86lZaOgDAnvI4x0lurfX8JM8/wqGfP8LYS/Lf/om7b7sl1gUAcDxzJxoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEr79noBnFzuf+bpOXjggr1eBgDAMXEnGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABK+/Z6AZxcrv7Itdn/7Iv2ehkAn3PNgQv2egnACcidaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJ6FvJzDx5Zn7uGM69966ff2lm7nfzre4mr+PcmfkHt/a8AADHm5M+omfHF/w+zMwpN+d6juLJST4X0Wut715rve+WmGhm9t3A4XOTiGgA4KR3Ukb0zOyfmffPzIuTvDvJD8/Mu2bmqpl53q4xH5iZX972/8bM3Gk7ds3M/MjMvC3JY2fmCTNz9cy8Z2ZeuGuep8zMB2fmLUm+dtf+l8/MP9r188d3bf/z7VpXzsyBbdx5SV45M1fMzB1n5s0zc942/mhzf3xmnr9d59KZudcNvB8vn5mfmpk3JXnhzDxkZv5gZi7fvn/5zNwhyb9M8rhtHY+bmTvPzEu39+7ymXnUMf7TAACcEE7KiN58eZJfSfIvkpyZ5CHZudP6oJl5xK4xF661HpDko0n+l13nf3Kt9XVJ3prkhUm+cTv/wTPz6Jk5I8nzshPP35zkRh+/mJm/n+TRSb5mrXVOkh9fa/1GkoNJnrjWOnet9Te7xt/7SHNvh++c5NLtOm9N8rQbmf7vJXnkWut/TfKBJI9Yaz0wyY8k+ddrrU9v26/a1vGqJM9J8vtrrQcn+YYkPzEzdz7C63r6zBycmYPXX3ftjb0NAADHvZM5ov9krXVpkm/Zvi7Pzl3ps5OctY3507XWJdv2K5J83a7zX7V9f3CSN6+1/tNa6zNJXpnkEUm+Ztf+T+8af0MemeRla63rkmSt9V9uZPzR5k6STyd57bZ9WZL9N3KtV6+1rt+2T0/y6pl5T5KfTvKVRznnW5I8e2auSPLmJF+U5D6HD1prXbjWOm+tdd4pdzr9RpYBAHD8u6HnX2/rPrF9nyQvWGv94u6DM7M/yTrsnN0/7z7/aA4//5DPZPsFZmYmyR12Xeto5xzJDc39X9dah651fW783/oTu7b/VZI3rbUes70Pb76B+b9zrfWHN2GtAAC3GSfznehDXp/ku2bmLkkyM2fOzN/djt1nZh66bT8hyduOcP47knz9zNxj+5DhE5K8Zdt//sx88czcPsljd51zTZIHbduPSnL7bfsN21oOPXt9923/x5KcVsx9rE5P8pFt+8m79h++jtcn+b7tF4HMzANvhrkBAI57J31Er7XekOTXkrx9Zq5O8hv521B8f5InzcxVSe6e5OePcP5fJPnfkrwpyZVJ3r3W+rfb/ucmeXuSN2bnUZFDXpKd+H1ndh77+MR2rdcl+e0kB7dHJJ61jX95kl849MHCG5v7WN6PzY8necHMXJJk918feVOS+x36YGF27ljfPslV26Mf/+pmmBsA4Lg3f/tf/Nlte4zhtWutr9rrtdyWnHrGWeuMJ71or5cB8DnXHLhgr5cAHKdm5rK11nlHOnbS34kGAIDWyfzBwhu01romyW3qLvTMPCef/2x2svNXOZ6/F+sBADhRieiTyBbLghkA4Bh5nAMAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABK+/Z6AZxc7n/m6Tl44IK9XgYAwDFxJxoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASvv2egGcXK7+yLXZ/+yL9noZnGCuOXDBXi8BAD6PO9EAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRRzAzz5iZO93A8V+amfvdmms6FjNz3sz8bHnOc2fmWbfUmgAATmT79noBx6lnJHlFkusOPzAzp6y1vvtWX9ExWGsdTHJwr9cBAHBbcdLfiZ6ZO8/MRTNz5cy8Z2Z+NMm9k7xpZt60jfn4zPzLmXlHkofOzJtn5rxdx56/nX/pzNxr23/f7ed3bed+fNecP7Ttv2pmnrft2z8z75+Zl8zMe2fmDTNzx+3Yl83MG7c53r1d+1dn5lG7rvnKmfn2o7zG82fmtdv2c2fmpdtr+OOZ+f5d454zM384M29M8uW79t93Zl43M5fNzMUzc/bM7Ntew/nbmBfMzPNvjn8TAIDj3Ukf0Um+Lcmfr7XOWWt9VZIXJfnzJN+w1vqGbcydk7xnrfU1a623HXb+nZNcutY6J8lbkzxt2/8zSX5mrfXg7XpJkpn5liRnJXlIknOTPGhmHrEdPivJ/7XW+sokf53kO7f9r9z2n5PkYUn+IskvJXnKds3Tt/2/cxNf89lJvnVbw4/OzO1n5kFJHp/kgUm+I8mDd42/MMn3rbUelORZSV681vpMkicn+fmZ+ebtfXzekSabmafPzMGZOXj9ddfexCUCABy/RHRydZJHzswLZ+bha60jVd71SV5zlPM/neS12/ZlSfZv2w9N8upt+9d2jf+W7evyJO/OTtCetR378Frrit3XmpnTkpy51vqtJFlrfXKtdd1a6y1Jvmxm/m6SJyR5zRa2N8VFa61PrbX+Ksl/THKvJA9P8lvbtT+a5LeTZGbukp1Af/XMXJHkF5Ocsa3lvUl+Ncm/S/Jda61PH2mytdaFa63z1lrnnXKn02/iEgEAjl8n/TPRa60Pbndh/0GSF8zMG44w7JNrreuPcon/utZa2/b1ufH3dJK8YK31i5+3c2Z/kk/t2nV9kjtu44/mV5M8MTt3kL/rRubd7fB5Dq15HWHs7ZL89Vrr3KNc6/7ZuWt+r2J+AIAT2kl/J3pm7p3kurXWK5L8ZJKvTvKxJKcd46Uvzd8+jvH4Xftfn+S7tju8mZkzt7vJR7TdFf6zmXn0Nv7UXX855OXZ+RDkobvCx+KtSR4zM3fc7n7/w13zf3hmHrvNPzNzzrb9HUm+OMkjkvzszNztGNcAAHBCOOkjOjt3Ut+5ParwnCQ/lp1ngH/30AcLv0DPSPKDM/PO7Dz+cG2SrLXekJ3HO94+M1cn+Y3ceLD/kyTfPzNXJfmDJP/ddq2/TPL+JC87hnVmu9a7k7wqyRXZeXTl4l2Hn5jkqTNzZZL3JnnUzNwjyYEkT11rfTDJz2XnOXAAgNu8+dsnEbg5bXeL/2attWbm8UmesNZ61I2d9wXMcXWSrz7Ks9zHnVPPOGud8aQX7fUyOMFcc+CCvV4CACehmblsrXXekY6d9M9E34IelOTnZmay88xw88zyjZqZRyZ5aZKfOlECGgDgtkJE30LWWhcnOecWvP4bk9xn976Z+dYkLzxs6IfXWo+5pdYBAHAyEtG3IWut12fng4sAANyCfLAQAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASvv2egGcXO5/5uk5eOCCvV4GAMAxcScaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEr79noBnFyu/si12f/si26Vua45cMGtMg8AcPJxJxoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiL6JDAz+2fmPTdhzD/e9fN5M/Ozt/zqAABOPCKaQ/Yn+VxEr7UOrrW+f++WAwBw/BLRx4HtLvAHZuaXZ+aqmfmNmbnTzHzTzFw+M1fPzEtn5tRt/DUz88KZeef29WXb/pfPzD/add2PH2Wui2fm3dvXw7ZDB5I8fGaumJlnzsz5M/Pa7Zy7z8z/s63t0pl5wLb/udu63jwzfzwzohsAOCmI6OPHlye5cK31gCQfTfKDSV6e5HFrrfsn2Zfkf941/qNrrYck+bkkLyrm+Y9Jvnmt9dVJHpfk0CMbz05y8Vrr3LXWTx92zvOSXL6t7X9P8iu7jp2d5FuTPCTJj87M7Yu1AACckET08eNP11qXbNuvSPJNST681vrgtu+Xkzxi1/hf3/X9ocU8t0/ykpm5Osmrk9zvJpzzdUl+NUnWWr+f5Itn5vTt2EVrrU+ttf4qO4F+r8NPnpmnz8zBmTl4/XXXFksFADg+7dvrBfA56xjGH9r+TLZfjGZmktzhCOc9M8lfJjlnG/vJmzDX3MD8n9q17/oc4X9Ta60Lk1yYJKeecVb7OgEAjjvuRB8/7jMzh+4oPyHJG5PsP/S8c5J/kuQtu8Y/btf3t2/b1yR50Lb9qOzcdT7c6Un+Yq312e2ap2z7P5bktKOs7a1JnpgkM3N+kr9aa330prwoAIDbIneijx/vT/KkmfnFJB9K8gNJLk3y6pnZl+RdSX5h1/hTZ+Yd2flF6Anbvpck+bcz884kv5fkE0eY58VJXjMzj03ypl1jrkrymZm5MjvPYl++65znJnnZzFyV5LokTzq2lwoAcGKbtfzX9b02M/uTvHat9VU3cfw1Sc7bnkM+oZx6xlnrjCe96FaZ65oDF9wq8wAAt00zc9n/z97dR3121vW9/3xNMBACAXzgRC2OVYQCwQADCoKCUM+y8aEKGJFWQCrFJ0SNPSxRBCtdYdkeKFqDwcYgWoooooCKPIPhMQGSgICsSjweddmCEMAolnCdP+49h9txnj6ZDMNMXq+1Zt2/e+9rX/vavzt/vGfPvn9Za+090D6PcwAAQMnjHJ8B1lpXJzmiu9Db+D3HbDEAAByWO9EAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUDr1eC+AG5ezv/DMXHbBucd7GQAAR8WdaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAonXq8F8CNy1V/fk32POGlx2z+qy8495jNDQCwjzvRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUKojemb2zMw7j8Vibmgzc8nMPOQzfc6jNTP3n5n7HGbMY2fmu8p5XzMze49udQAAJ59Tj/cCuEHcP8nHkrzhYAPWWs/6tK0GAOAkd30f5zhlZp49M++amT+YmZvtvms5M587M1dvrx85My+amRfPzPtn5gdm5kdm5u0z86aZuc027ntm5q0zc8XM/ObMnL5tv2Rmnjkzb5iZPznUXeDZ8fMz80cz89Ikn79r3z1m5rUzc/nMvGxmzpqZfzYzb9k1Zs/MXHmw8Qc43wO367hqZi6emdO27VfPzNNm5i3bny/bdS0Xzsyrt2v52u24d8/MJbvm/fqZeePMvG1mXjAzZ+ya9ynb9qtm5o4zsyfJY5P88My8Y2bud5D35skzc/72+jW71vfH+47Zfo7/fWaunJnnJ7nZodY0M188M+/bft6fNTOvn5mvP8C5HzMzl83MZddde83BfnwAACeM6xvRt0/yX9Zad07y4SQPPsz4uyT5ziT3SvLUJNeute6W5I1J9j1i8MK11j3XWl+R5N1JHr3r+LOS3DfJNya54BDn+dYkd0hydpLvSXKfJJmZmyT5uSQPWWvdI8nFSZ661np3ks+emX+6HX9ekl8/2PjdJ5qZmya5JMl5a62zs3NX/3t3DfnIWuteSX4+yTN2bb91kq9L8sNJXpzk6UnunOTsmTlnZj43yU8kedBa6+5JLkvyI7uO/8C2/cIk56+1rk7yrCRPX2uds9Z6/SHen91O3db3+CQ/tW373uz8bO66Xe89tms94JrWWn+a5Gnb+X80yR+ttf5g/xOttS5aa+1da+095fQzj3B5AACfua7v4xzvX2u9Y3t9eZI9hxn/6rXWR5N8dGauyU48JslVSe66vb7LzPxMklslOSPJy3Yd/6K11ieT/NHM3PYQ5/maJM9ba12X5C9m5lXb9jtkJ+RfPjNJckqSv9z2/XqSb89OnJ+3/TnU+H3ukJ334Y+375+T5PvzqWB+3q6vT9913IvXWmtmrkryV2utq5JkZt6Vnffxi5LcKcml27k/Ozt/2djnhdvXy5N82yHei8PZPc+e7fXXJHlmkqy1rtx3Vz7JVx1sTWutX5qZh2bnbvg5R7EeAIATxvWN6I/ven1ddv7Z/xP51J3tmx5i/Cd3ff/JXWu4JMm/XGtdMTOPzM5zvgc6fg6ztnWAbZPkXWutex9g3/OTvGBmXphkrbXeNzNnH2L89VnH7te7r33/9+XU7LyfL19rPewg8+475roc3TPtB5vnYO/fAde0PXbzRdu3ZyT56FGsCQDghHBDfsTd1dn++T/J9fn0ilsk+cvtUYqHX881vC7Jd8zMKdszzA/Ytr83yefNzL2Tncc7ZubOSbLW+h/ZCcmfzE5QH3L8Lu9Jsmff885J/nWS1+7af96ur7vvJB/Om5J89a7nqE+fmS8/zDEfzc77d7Rel+29n5m75FP/SnCoNT0tya8leVKSZ98AawAA+Ix3Q0b0f0zyvTPzhiSfez2O/8kkb07y8uwE6vXxW0nel53HRC7MFrVrrb/PTtg/bWauSPKObM9Lb56f5F9l59GOIxmftdbfJXlUdu5iX5WdO8m7PwHjtJl5c5Ifys7zz0dkrfW/kjwyyfO2xynelOSOhznsxUm+9VC/WHiELkxyxnbef5fkLYda08x8bZJ7JnnaWuvXkvz9zDzqKM4PAHBCmLUO9K/3HI3Z+WSSvWutDxzvtXymOe2s26+zHvGMYzb/1Rece8zmBgBuXGbm8rXWAf+fGf6PhQAAUDoh/2cr2y/+PXe/zR9fa33l8VjP/tZae47XuWfmiUkeut/mF6y1nnqg8QAA9E7IiN4+Fu6c472Oz0RbLAtmAIBjyOMcAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFA69XgvgBuXs7/wzFx2wbnHexkAAEfFnWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAonXq8F8CNy1V/fk32POGlx2Tuqy8495jMCwCwP3eiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkoo/CzOyZmXcewZjv3PX93pl55jFc05Nn5vwbeM7fnZlbbX++74acGwDgRCSij709Sf7/iF5rXbbWetzxW05vrfUv1lofTnKrJCIaALjRO6kjersL/J6Zec7MXDkzvzEzp8/MA2fm7TNz1cxcPDOnbeOvnpmnzcxbtj9ftm2/ZGYesmvejx3kXK+fmbdtf+6z7bogyf1m5h0z88Mzc/+Zecl2zG1m5kXb2t40M3fdtj95W9drZuZPZuaQ0T0zT5yZ987MK5LcYdf2L52Z35+Zy7e13XHX9TxzZt6wzf+QbftZM/O6ba3vnJn77XpfPne7li/d9v/szDx3Zr5l1/l+bWa+uf5BAQCcYE7qiN7cIclFa627JvlIkh9JckmS89ZaZyc5Ncn37hr/kbXWvZL8fJJnFOf5n0n++Vrr7knOS7LvkY0nJHn9WuuctdbT9zvmKUnevq3tx5P8yq59d0zyfya5V5KfmpmbHOikM3OPJN+R5G5Jvi3JPXftvijJD6617pHk/CS/sGvfWUnum+QbsxPHyc4d85ettc5J8hVJ3rHf6Z6Q5H9s1/JjSX4pyaO2dZyZ5D5JfvcAa3zMzFw2M5ddd+01B7oMAIATyo0hov9srXXp9vpXkzwwyfvXWn+8bXtOkq/ZNf55u77euzjPTZI8e2auSvKCJHc6gmPum+S5SbLWelWSz9liNEleutb6+FrrA9kJ9NseZI77Jfmttda1a62PJPmdJJmZM7ITtS+YmXck+cXshPM+L1prfXKt9Ue75n5rkkfNzJOTnL3W+uihFr/Wem2SL5uZz0/ysCS/udb6xAHGXbTW2rvW2nvK6Wf+o3kAAE40px7vBXwarKMYv+/1J7L9hWNmJslnH+C4H07yV9m5g/tZSf7uCM41hzj/x3dtuy6H/lkd6Bo/K8mHt7vKB7J7/kmStdbrZuZrkpyb5Lkz87NrrV854NGf8twkD8/O3fDvPsxYAICTwo3hTvTtZmbfHeWHJXlFkj37nndO8q+TvHbX+PN2fX3j9vrqJPfYXn9Ldu467+/MJH+51vrkNucp2/aPJrnFQdb2uuwEaGbm/kk+sN1NbrwuybfOzM1m5hZJvilJtnnePzMP3eafmfmKQ000M1+c5H+utZ6d5L8muft+Qw50LZckefx2zneVawcAOCHdGO5EvzvJI2bmF5O8L8kPJXlTdh5zODU7jzA8a9f402bmzdn5C8bDtm3PTvLbM/OWJK9M8jcHOM8vJPnNLVpfvWvMlUk+MTNXZCc4377rmCcn+eWZuTLJtUke0V7cWuttM/P87Dy//KdJXr9r98OTXDgzP5Gd8P/vSa44xHT3T/JjM/O/k3wsyXftd64Pzsyl28f6/d5a68fWWn81M+9O8qJ27QAAJ6pZq33a4cQxM3uSvGStdZcjHH91kr3bc8gcgZk5PclVSe6+1jrsbw2edtbt11mPeMYxWcvVF5x7TOYFAG6cZubytdbeA+27MTzOwTEyMw9K8p4kP3ckAQ0AcLI4qR/nWGtdneSI7kJv4/ccs8UcpZn5nOw8SrK/B661PvjpXk+SrLVekeR2x+PcAADH00kd0SeTLZTPOd7rAADA4xwAAFAT0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQOvV4L4Abl7O/8MxcdsG5x3sZAABHxZ1oAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiderwXwI3LVX9+TfY84aU36JxXX3DuDTofAMDhuBMNAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRF9A5mZPTPzzuO9jhvazPz0zDxoe/34mTn9eK8JAOB4E9EnuJk59VjOv9Z60lrrFdu3j08iogGAGz0RfYRm5idn5j0z8/KZed7MnD8z95iZK2bmjUm+f9fYR87Mb8/M78/Me2fmpw4x755t3ufMzJUz8xv77vZu8792Zi6fmZfNzFnb9tfMzH+Ymdcm+aGDzHvbmfmtbX1XzMx9tu0v2uZ718w8Ztf4j83Mf5qZt83MK2fm87btl8zMQ2bmcUm+IMmrZ+bV274LZ+ayba6nHOIaH7ONu+y6a6858jcdAOAzlIg+AjOzN8mDk9wtybcl2bvt+uUkj1tr3fsAh90rycOTnJPkodscB3OHJBette6a5CNJvm9mbpLk55I8ZK11jyQXJ3nqrmNutdb62rXWfzrInM9M8tq11lckuXuSd23bv3ubb2+Sx83M52zbb57kbWutuyd5bZJ/EP5rrWcm+YskD1hrPWDb/MS11t4kd03ytTNz1wMtZK110Vpr71pr7ymnn3mItwEA4MQgoo/MfZP89lrrb9daH03y4uxE563WWq/dxjx3v2Nevtb64Frrb5O8cJvjYP5srXXp9vpXt7F3SHKXJC+fmXck+YkkX7TrmOcfZs1fl+TCJFlrXbfW2ncL+HEzc0WSNyX5J0luv23/5K45963hcL59Zt6W5O1J7pzkTkdwDADACe+YPk97EpkDbPubJOsQx+y/rx07Sd51kLvc+85fmZn7J3lQknuvta6dmdckuekRrmn/ub4kyflJ7rnW+tDMXHKIuQAATiruRB+ZP0zyTTNz05k5I8m52/ZrZmbfHduH73fMP5+Z28zMzZL8yySX5uBuNzP7Yvlh2/nem+Tz9m2fmZvMzJ2LNb8yyfdux54yM7dMcmaSD20BfcckX7Vr/Gclecj2+ju3Nezvo0lusb2+ZXZC/pqZuW2SbyjWBgBwQnMn+gistd46M7+T5Iokf5rksiTXJHlUkotn5tokL9vvsD/MziMeX5bkv621LjvEKd6d5BEz84tJ3pfkwrXW38/MQ5I8c2bOzM7P6hn51LPNh/NDSS6amUcnuS47Qf37SR47M1dmJ9LftGv83yS588xcvl3beQeY86Ikvzczf7nWesDMvH1bz5/k0H9JAAA4qcxah/xXezYzc8Za62PbJ2e8Lslj1lpvO8jYRybZu9b6gSOYd0+Sl6y17nJDrrc1Mx9ba51xrM9z2lm3X2c94hk36JxXX3Du4QcBAJRm5vLtQxT+EXeij9xFM3On7Dz3+5yDBTQAACc/EX2E1lrfWYy9JMklu7dtHyX3ygMMf+DR3IWemScmeeh+m1+w1nrqgcYfzKfjLjQAwMlCRH+arLU+mJ3PjL6h531q/uHnRwMAcIz5dA4AACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAonXq8F8CNy9lfeGYuu+Dc470MAICj4k40AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJROPd4L4Mblqj+/Jnue8NKjnufqC869AVYDAHD9uBMNAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AAKWTJqJn5skzc/7M/PTMPOgwY18zM3uLuc+ZmX9x9Ks8ftprBgDg4E493gu4oa21nnQMpj0nyd4kv3sM5j6omTllrXXdp/OcAAAc3gl9J3pmnjgz752ZVyS5w7btkpl5yPb6STPz1pl558xcNDOz6/B/NTNv2Pbdaxt/85m5eDvm7TPzLTPz2Ul+Osl5M/OOmTnvQOO24+88M2/Zxl05M7c/yLr3zMx7ZuY527jfmJnTt31Xb+v+wyQPnZmvn5k3zszbZuYFM3PGzHzDzPz6rvnuPzMv3l5fODOXzcy7ZuYpBzn/P5pz17mfsm2/ambuuG0/Y2Z+edt25cw8+FDzAACc7E7YiJ6ZeyT5jiR3S/JtSe55gGE/v9a651rrLkluluQbd+27+VrrPkm+L8nF27YnJnnVWuueSR6Q5GeT3CTJk5I8f611zlrr+QcaNzM3T/LYJP95rXVOdu5c/7+HuIQ7JLlorXXXJB/Z1rHP36217pvkFUl+IsmD1lp3T3JZkh9J8vIkX7WdM0nOS/L8fdew1tqb5K5JvnZm7rrf+/a5B5lznw9s2y9Mcv627SeTXLPWOntb76uOYJ7d53zMFvaXXXftNYd4SwAATgwnbEQnuV+S31prXbvW+kiS3znAmAfMzJtn5qokX5fkzrv2PS9J1lqvS3LLmblVkq9P8oSZeUeS1yS5aZLbHWDeg417Y5Ifn5n/K8kXr7X+9hDr/7O11qXb619Nct9d+/YF8VcluVOSS7dzPWKb9xNJfj/JN83MqUnOTfLb2zHfPjNvS/L27XrvtN95Dzjnrv0v3L5enmTP9vpBSf7LvgFrrQ8dwTzZNf6itdbetdbeU04/8yBvBwDAieNEfyZ6HWzHzNw0yS8k2bvW+rOZeXJ2Yvdgx64kk+TBa6337jfXV+4//YHGJXn3zLw5O1H7spn5N2utVx3h2nd//ze7zvPytdbDDnD885N8f5K/TvLWtdZHZ+ZLsnP3+J5rrQ/NzCX5h9d8uDmT5OPb1+vyqf8+5gDrPdw8AAAnrRP5TvTrknzrzNxsZm6R5Jv2278vHj+wPav7kP32n5ckM3Pf7DyqcE2SlyX5wX3PTs/M3baxH01yi13HHnDczPzTJH+y1npmdu6M/4NHKfZzu5m59/b6YUn+8ABj3pTkq2fmy7b5T5+ZL9/2vSbJ3ZN8Tz515/qW2Qnwa2bmtkm+oZzzYP4gyQ/s+2Zmbn095wEAOCmcsBG91npbduLxHUl+M8nr99v/4STPTnJVkhcleet+U3xoZt6Q5FlJHr1t+/fZeQb6ypl55/Z9krw6yZ32/WLhIcadl+Sd2+MNd0zyK4e4hHcnecTMXJnkNtl5Bnn/a/xfSR6Z5HnbuDdt82b71I6XZCeUX7JtuyI7j3G8KzvPeV/azHkIP5Pk1tsvYV6R5AHXcx4AgJPCrHXQJyI4RmZmT5KXbL/weKNy2lm3X2c94hlHPc/VF5x79IsBADiEmbl8+8CGf+SEvRMNAADHy4n+i4Wf0Wbmc5K88gC7HnhjvAsNAHCyENHH0Frrg9n5vx0CAHAS8TgHAACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlE493gvgxuXsLzwzl11w7vFeBgDAUXEnGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKpx7vBXDjctWfX5M9T3jpUc1x9QXn3kCrAQC4ftyJBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIPoyZ+dj29Qtm5jd2bX/ezFw5Mz98/FZ35Gbm8TNz+vFeBwDAyeDU472AE8Va6y+SPCRJZub/SHKftdYXH99VVR6f5FeTXHuc1wEAcMJzJ/oIzcyemXnn9u0fJPn8mXnHzNxvZr50Zn5/Zi6fmdfPzB0PMc/nzcxvzsxbtz9fvW3/7Zn5ru31v52ZX9tev2ZmnjEzb5iZd87MvbbtN5+Zi7c53j4z37JtP2Vm/uPMXLXdKf/BmXlcki9I8uqZefU27sKZuWxm3jUzT9m1vqtn5ikz87Ztjjtu28+YmV/eNe+DZ+bRM/P0Xcd+z8z83we45sds57rsumuvOZofAwDAZwR3oq+fb07ykrXWOUkyM69M8ti11vtm5iuT/EKSrzvIsf85ydPXWn84M7dL8rIk/yzJY5JcOjPvT/KjSb5q1zE3X2vdZ2a+JsnFSe6S5IlJXrXW+u6ZuVWSt8zMK5J8V5IvSXK3tdYnZuY2a62/npkfSfKAtdYHtjmfuG0/JckrZ+aua60rt30fWGvdfWa+L8n5Sf5Nkp9Mcs1a6+ztmm+d5O+TXDkz/26t9b+TPCrJv93/gtdaFyW5KElOO+v268jeYgCAz1wi+ijNzBlJ7pPkBTOzb/NphzjkQUnutGvsLWfmFmutv5qZJyV5dZJvXWv99a5jnpcka63Xzcwtt2j++iTfPDPnb2NumuR22/zPWmt9Yjtm9zy7ffvMPCY7/w2cleROSfZF9Au3r5cn+bZd6/6OfQevtT60Xf+rknzjzLw7yU3WWlcd4toBAE4KIvrofVaSD++7K32E4++91vrbA+w7O8kHs/PoxW77371dSSbJg9da7929Y3bq/JB3e2fmS7Jzh/mea60Pzcwl2YnwfT6+fb0un/pv5GDz/lKSH0/yniS/fKjzAgCcLDwTfZTWWh9J8v6ZeWiyE7Ez8xWHOOQPkvzAvm9m5pzt672SfEOSuyU5fwvdfc7bxtw3O49UXJOdx0B+cIvmzMzdds3/2Jk5ddt+m237R5PcYnt9yyR/k+Sambntdt7D2X/dt96u/81J/kmS78x2xxwA4GQnom8YD0/y6Jm5Ism7knzLIcY+Lsne7Zfz/ig7wXtakmcn+e7tU0B+NMnF+wI5yYdm5g1JnpXk0du2f5/kJtl5Jvmd2/fJzp3h/2fbfkV24jbZeSb592bm1WutK5K8fVvrxUkuPYJr/Jkkt95+ufGKJA/Yte/Xk1y67xEPAICT3azl97w+k83Ma5Kcv9a67Hiv5WBm5iXZ+WXJVx5u7Gln3X6d9YhnHNX5rr7g3KM6HgDgSMzM5WutvQfa504019vM3Gpm/jjJ3x5JQAMAnCz8YuExMjNPTPLQ/Ta/YK311Gaetdb9b7BF3cDWWh9O8uXHex0AAJ9uIvoY2WK5CmYAAE4MHucAAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAb+P3buPlrXu67v/OdrDgSDmAGUrlNaTGvDgzYYJKgIjWBAS7OKIMxkFJn4sKTOEhm7hrpw8AF0tEdpLVhHS6BAdDrIQh6GRSoJ8ihQIAFCEhB0tUTHFLFWGksBtfE3f+zr6Dbus3M+Jwn7nOT1Wmuvfd/X4/fe+ed9rvz2BgBKIhoAAEoiGgAASiIaAABKIhoAAEqHDnoA7lzOue+ZuerIhQc9BgDAreJJNAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACUDh30ANy5XHvDjTnrWZed8PnXH7nwNpwGAODEeBINAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AAKU7fETPzPUz80Xb63cd9Dz72T3rCZz7/TNzxq73n7rtJvvza547M//gtr4uAMCp5g4V0TNzaL/9a62v/VzNcgC+P8kZt3TQLbmFn+G5SUQ0AHCnd9JG9Mz8LzNzzcx8cGZ+aWb+4cy8Z2Y+MDO/NjN/bTvuOTNzycxckeQXZ+beM3PFdtwLk8yua35q+z4z87yZuW5mrp2Zi7btr9j9pHVmXjYzT5qZs2bm12fm/dvX1277D8/M22fm6u1af2/b/ve34z44M2/a5zPuN+u3zcx7t2u/cGZO27b/wsxcNTMfmpnnbtuekeSvJ3nLzLxl1zV+Ypvh3Ud/XseY42Uz8zPbuT81M181M+/a5nrXzDxgZu6a5MeSXLTNdNHM3H1mXjIzV27HftPx/vcFADiVnZQRPTNfnuTZSb5+rfUVSf63JO9I8jVrrYck+eUkP7DrlIcm+aa11rcm+dEk79iOe12S++1xi2/OzlPVr0jymCTPm5nD23WPBvVdk1yQ5N8m+f0kj11rfeW2/2e363xrksvXWkevdfXMfHGSFyV50jb7/7jPR91z1pl50HafR2zXvinJU7Zznr3WOi/Jg5N83cw8eK31s0n+Y5JHr7UevR139yTv3mZ4e5Lv3meOJLl/ksestf73JB9Jcv42148k+cm11p9sr1+x1jp3rfWK7Pw3evNa62FJHr39HO9+8wvPzNO28L/qpk/feAtjAACc/PZd/nCAvj7Jr6y1/iBJ1lp/ODPnJHnFFrt3TfKxXce/bq31me31+dmJ5Ky1LpuZT+5x/Ucmefla66Ykn5iZtyV5WJJfTfKzM3N6kr+f5O1rrc/MzJlJfm5mzs1O0N5/u86VSV4yM3dJ8tq11tUz86jtvI8dnX2fz3msWS/Izj8MrpyZJPn87IR8kvxPM/O07Py3O5zky5Jcs8e1/yTJ67fX70vy2H3mSJJXbj+PJDkzyaUzc3aSleQuxzjnG5I8fmaeub2/W3b+IfAbuw9aa12S5JIkOf3w2esW5gAAOOmdrBE92Ym33f5lkp9Za71uC9Xn7Nr332527C2F2uy1ca312Zl5a5JvzM6T4Jdvu/5xkk9k52nz5yX57Hb822fm/CQXJvmlmXlekv9yHPe/pVknyaVrrR/8Sxtn/laSZyZ52FrrkzPzsuyE617+dK119No35Zb/W+/+Gf54krestZ44M2cleesxzpnsPHH/6C1cGwDgDuWkXM6R5E3ZeeJ67ySZmXtl5+noDdv+i/c59+3Zlj7MzOOS3PMYx1w0M6dtyy/OT/Lebd8vJ/mOJH8vyeXbtjOTfHyt9WdJnprk6PrkL0ny+2utFyX510m+Msm/y84yi7+1a/Z21jclefLM3OfoNbZ7fWF2YvfGbY3z43Zd678mucc+92rs/ll/+z73uDzJ9832uHxmHnIb3R8A4KR2Ukb0WutDSX4iydtm5oNJfiY7T55fOTO/nuQP9jn9uUnOn5n3Z2e5we/sccxrsrME4oNJ3pzkB9Zav7ftuyI7Uf1r2zrgJPn5JBfPzLuzs5Tj6FPbR2VnHfQHkjwpyQvWWv8pydOSvHqb/RXtrGutDyf5oSRXzMw1Sd6Y5PBa64NJPpDkQ0lekuSdu651SZJf3f2LhbfCTyf5pzPzzmz/YNi8JcmXHf3Fwuw8sb5Lkmtm5rrtPQDAHd78xf/xh9vf6YfPXocvfv4Jn3/9kQtvu2EAAPYxM+/b/qDDX3FSPokGAICT2cn6i4V3KDPzHdn5M327vXOt9b2f4zmenb/6J/deudb6ic/lHAAApzoR/Tmw1nppkpeeBHP8RHbWmgMAcCtYzgEAACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAClQwc9AHcu59z3zFx15MKDHgMA4FbxJBoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASocOegDuXK694cac9azL6vOuP3Lh7TANAMCJ8SQaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiL6AMzMo2bm9Qc9R5LMzHNm5pkHPQcAwKlERHPCZua0g54BAOAgiOgTNDNnzcxHZubFM3PdzPybmXnMzLxzZn5rZr5q+3rXzHxg+/6APa5z95l5ycxcuR33Tfvc88tn5r0zc/XMXDMzZx/PHNu595qZ127nvXtmHrzH9b97Zn51Zj5/Zr5t171eeDSYZ+ZTM/NjM/OeJA+fmSMz8+Htuv/sNvwRAwCctET0rfN3krwgyYOTPDDJtyZ5ZJJnJvk/knwkyflrrYck+ZEkP7nHNZ6d5M1rrYcleXSS583M3Y9xv+9J8oK11rlJzkvyu8c5R5I8N8kH1loP3rb94u4Lz8zTk/zDJE9IclaSi5I8YrvXTUmesh169yTXrbW+OsmHkzwxyZdv1/0/9xp6Zp42M1fNzFU3ffrGY3w0AIBTx6GDHuAU97G11rVJMjMfSvKmtdaamWuzE6JnJrl0Zs5OspLcZY9rfEOSx+9al3y3JPdL8ht7HPvvkjx7Zv5GklevtX5rZo5njmQnqp+UJGutN8/MvWfmzG3fU7MT5E9Ya/3pzFyQ5KFJrtyu//lJfn879qYkr9pe/1GSzyZ58cxclmTPdd5rrUuSXJIkpx8+e+11DADAqcST6Fvnj3e9/rNd7/8sO/9A+fEkb1lr/d3sPOW92x7XmCRPWmudu33db621V0BnrfX/JHl8ks8kuXxmvv445zh6n79yye37ddmJ7b+x69hLd830gLXWc7Z9n11r3bTN89+TfFV2ovoJSd6w19wAAHc0Ivr2dWaSG7bX336MYy5P8n2zPfKdmYcc62Iz87eT/Ie11s8meV12lm8cr7dnW5IxM49K8gdrrT/a9n0gyT9K8rqZ+etJ3pTkyTNzn+34e83Ml+wxzxckOXOt9W+TfH+Sc4t5AABOWSL69vXTSf7pzLwzybH+ksWPZ2eZxzUzc932/lguSnLdzFydnbXPv7jPsTf3nCTnzcw1SY4kuXj3zrXWO7Kzhvqy7Czd+KEkV2zHvzHJ4T2ueY8kr9+OeVuSf1zMAwBwypq1LFHlc+f0w2evwxc/vz7v+iMX3vbDAADsY2bet9Y6b699nkQDAEDJX+c4Cc3MNyb5qZtt/tha64kHMQ8AAH+ZiD4JrbUuz84vHAIAcBKynAMAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKhw56AO5czrnvmbnqyIUHPQYAwK3iSTQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlA4d9ADcuVx7w40561mX1eddf+TC22EaAIAT40k0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURPQpamY+dRtf7wkz82W73v/YzDzmtrwHAMAdhYjmqCck+fOIXmv9yFrr1w5uHACAk5eIPsXNjufNzHUzc+3MXLRr3w9s2z44M0e2bd89M1du2141M2fMzNcmeXyS583M1TPzpTPzspl58nbOBTPzge1aL5mZ07ft18/Mc2fm/du+Bx7EzwAA4HNNRJ/6vjnJuUm+IsljshPCh2fmcdl5uvzVa62vSPLT2/GvXms9bNv2G0m+a631riSvS/JP1lrnrrX+/dGLz8zdkrwsyUVrrXOSHEryv+66/x+stb4yyS8keebt9zEBAE4eIvrU98gkL19r3bTW+kSStyV5WHaC+qVrrU8nyVrrD7fj/+7M/PrMXJvkKUm+/Bau/4AkH1tr/eb2/tIk5+/a/+rt+/uSnLXXBWbmaTNz1cxcddOnb+w+HQDASUhEn/pmn+1rj+0vS/L07anyc5Pc7QSvf9Qfb99vys5T6r9irXXJWuu8tdZ5p51x5i1cDgDg5CeiT31vT3LRzJw2M1+cnafE701yRZLvnJkzkmRm7rUdf48kH5+Zu2TnSfRR/3Xbd3MfSXLWzPyd7f1Ts/O0GwDgTktEn/pek+SaJB9M8uYkP7DW+r211huys875qpm5On+xXvmHk7wnyRuzE8hH/XKSf7L9AuGXHt241vpsku9I8sptCcifJflXt+9HAgA4uc1ae/0ff7h9nH747HX44ufX511/5MLbfhgAgH3MzPvWWufttc+TaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKB066AG4cznnvmfmqiMXHvQYAAC3iifRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFA6dNADcOdy7Q035qxnXVadc/2RC2+naQAATown0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0SeJmbl+Zr5on/3/w8z8ysx8ZGZ+Y2Yevm1/zszcMDNXb1//4ATu/aiZef2JzAUAcGd06KAH4Li9IMkb1lpPnpm7Jjlj175/sdb6Zwc0118yM6ettW466DkAAG5PnkSfgJl57cy8b2Y+NDNP27Z918z85sy8dWZeNDM/t23/4pl51cxcuX09Ytt+75m5YmY+MDMvTDL73O8Lk5yf5F8nyVrrT9Za/+UE5r7bzLx0Zq7d7vvoPY455lwz820z897tifcLZ+a0bfunZubHZuY9SR7ezgUAcKoR0SfmO9daD01yXpJnzMx9k/xwkq9J8tgkD9x17Auy86T4YUmelOTF2/YfTfKOtdZDkrwuyf32ud/fTvKfkrx0i9sXz8zdd+1/+sxcMzMvmZl77nOd702StdY5Sb4lyaUzc7ebHbPnXDPzoCQXJXnEWuvcJDclecp2zt2TXLfW+uq11jtuftOZedrMXDUzV9306Rv3GQ8A4NQgok/MM2bmg0neneRvJnlqkrettf5wrfWnSV6569jHJPm5mbk6O1H6hTNzj+w8Wf6/k2StdVmST+5zv0NJvjLJL2xx+9+SPGvb9wtJvjTJuUk+nuSf73OdRyb5pe2eH0ny20nuf7NjjjXXBUkemuTK7bNckJ24T3aC+lXHuula65K11nlrrfNOO+PMfcYDADg1WBNdmplHZSeMH77W+vTMvDXJR5M86BinfN527Gdudp0kWcd5299N8rtrrfds738lW0SvtT6x65ovSrLnLwgePeQ477fXXJPk0rXWD+6x77PWQQMAdyaeRPfOTPLJLaAfmJ0lHGck+bqZuefMHMrOso2jrkjy9KNvZubc7eXbsy2HmJnHJTnmMoy11u8l+f9m5gHbpguSfHg79/CuQ5+Y5Lp9Zt99z/tnZ6nGR/c5Zvdcb0ry5Jm5z7bvXjPzJfvcCwDgDsuT6N4bknzPzFyTnQB9d5Ibkvxkkvck+Y/ZCdyji3+fkeT/2o4/lJ1I/Z4kz03y8pl5f5K3JfmdW7jv9yX5N9tf5vgPSb5j2/7TW5ivJNcn+Uf7XOPnk/yrmbk2yX9P8u1rrT/enooftedca60Pz8wPJbliZj4vyZ9mZ431b9/C3AAAdziz1vGuKGA/M/MFa61PbU+iX5PkJWut1xz0XCeb0w+fvQ5f/PzqnOuPXHj7DAMAsI+Zed9a67y99lnOcdt5zvYLd9cl+ViS1x7oNAAA3G4s57iNrLWeeWuvMTP3zs7a45u7YK31n4vrfGOSn7rZ5o+ttZ54a+YDAGCHiD6JbKF87m1wncuTXH6rBwIAYE+WcwAAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEDp0EEPwJ3LOfc9M1cdufCgxwAAuFU8iQYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCA0qGDHoA7l2tvuDFnPeuy6pzrj1x4O00DAHBiPIkGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIPknMzPUz80XH2PeAmbl619cfzcz3b/vuNTNvnJnf2r7f8wTu/aiZeX07FwDAnZWIPgWstT661jp3rXVukocm+XSS12y7n5XkTWuts5O8aXt/YGbmtIO8PwDA54KIPgEz89qZed/MfGhmnrZt+66Z+c2ZeevMvGhmfm7b/sUz86qZuXL7esS2/d4zc8XMfGBmXphkjvP2FyT592ut397ef1OSS7fXlyZ5wj5z321mXjoz1273ffQexxxzrpn5tpl57/Y0/IVHg3lmPjUzPzYz70ny8OP8HAAApywRfWK+c6310CTnJXnGzNw3yQ8n+Zokj03ywF3HviDJv1hrPSzJk5K8eNv+o0nesdZ6SJLXJbnfcd77f07y8l3v/9pa6+NJsn2/zz7nfu923DlJviXJpTNzt5sds+dcM/OgJBclecT2RPymJE/Zzrl7kuvWWl+91nrHzW86M0+bmatm5qqbPn3jcX5MAICT16GDHuAU9YyZeeL2+m8meWqSt621/jBJZuaVSe6/7X9Mki+b+fMHul84M/dIcn6Sb06StdZlM/PJW7rpzNw1yeOT/OAJzv3IJP9yu+dHZua3d8151LHmuiA7S0mu3D7L5yf5/W3fTUledaybrrUuSXJJkpx++Ox1grMDAJw0RHRpZh6VnTB++Frr0zPz1iQfTfKgY5zyeduxn7nZdZKkDcrHJXn/WusTu7Z9YmYOr7U+PjOH8xdhu+f4x3mfveaaJJeutfYK+M+utW46zmsDAJzyLOfonZnkk1tAPzA7SzjOSPJ1M3PPmTmUnWUbR12R5OlH38zMudvLt2dbDjEzj0tyPH9V41vyl5dyJDtLLi7eXl+c5P/d5/zd97x/dpZqfHSfY3bP9aYkT56Z+2z77jUzX3IcMwMA3OGI6N4bkhyamWuS/HiSdye5IclPJnlPkl9L8uEkRxf/PiPJeTNzzcx8OMn3bNufm+T8mXl/km9I8jv73XRmzsjOeutX32zXkSSPnZnf2vYf2ecyP5/ktJm5Nskrknz7WuuPb3bMnnOttT6c5IeSXLF99jcmObzfzAAAd1SzliWqt4WZ+YK11qe2J9GvSfKStdZrbum8O5vTD5+9Dl/8/Oqc649cePsMAwCwj5l531rrvL32eRJ923nOzFyd5LokH0vy2gOdBgCA241fLLyNrLWeeWuvMTP3zs7a45u7YK31n4vrfGOSn7rZ5o+ttZ641/EAAHRE9ElkC+Vzb4PrXJ7k8ls9EAAAe7KcAwAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEqHDnoA7lzOue+ZuerIhQc9BgDAreJJNAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJQOHfQA3Llce8ONOetZl1XnXH/kwttpGgCAE+NJNAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlET0/8/evUbddtV1nv/9TTBcKwgqHRGIYhQFQmICiqAdUbHLUAgKRuWqDihsNRY2aBQVSkwZC1uBKhVpS26iQFAoBFtA7gKBJJALoEgXhBqNVlveIhjlEma/eFbg8XDOyfklOZycnM9nDIZ7rz3XmnM92xffrMwNAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtGHmZl5/cycur3+w5m55SFeEgDAEefoQ70Arrm11rcd6jUAAByJPIn+LJiZ42fmz2bmN2fmXTPz/Jn55pl588y8b2buMTM3m5nfmpnzZ+adM/Pt27k3mZkXzMwlM/PCJDfZdd3LZubzt9cvnZkLZ+bdM/PoXWM+MjNnz8zFM3PezNxmP+v8NzPztm3+P75q7Mx8wcy8embeMTO/MTMf3DXvQ2fm7TNz0fbZUQfpzwgAcL0hoj97vizJ05KcmOROSb43yb2TPC7JTyV5QpLXrrXunuQbkzxlZm6W5AeTXLHWOjHJ2UlO2cf1v3+tdUqSU5OcOTO33o7fLMl5a627JXljkkftZ41/kuRr11onJ3lBkh/fjj9xW9tXJ3lJktsnycx8ZZIzktxrrXVSkiuTPGTPi87Mo2fmgpm54MorLt/P9AAAhwfbOT57PrDWujRJZubdSV6z1lozc2mS45N8cZL7z8zjtvE3zk6sfkOSpyfJWuuSmblkH9c/c2YeuL2+XZITkvxNko8lefl2/MIk37KfNX5xkhfOzHFJPjfJB7bj907ywG0NfzQzf7cd/6bsRP35M5PsPCX/qz0vutZ6ZpJnJskxx52w9jM/AMBhQUR/9nx01+tP7nr/yex8D1cm+c611nt3n7TF6X7Dc2ZOS/LNSe651rpiZl6fnQhPko+vta46/8rs/zv/T0l+ea31su2aT7pqin1NneQ5a62f3N/6AABuaGznuP54ZZIfma2aZ+bk7fgbs22RmJm7ZGc7yJ6OTfJ3W0DfKcnXXsM1HJvkQ9vrR+w6/idJvmtbw32TfN52/DVJHjQzX7h9dquZucM1nBsA4LAhoq8/npzkRkkumZl3be+T5NeT3HzbxvHjSd6+l3P/KMnR25gnJznvGq7hSUnOnZk3JfnrXcf/fZL7zsw7kvzrJH+Z5MNrrfck+ekkr9rmfnWS467h3AAAh4359L/ph72bmWOSXLnW+sTM3DPJr28/JKwdc9wJ67hHPLU657JzTr8mUwEAXCszc+Fa69S9fWZPNAfi9kleNDOfk50fKu7vv+EDAOAGT0QfgWbmCUkevMfhc9daZ+9t/FrrfUlO3ttnAABHIhF9BNpiea/BDADA1fPDQgAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACgdfagXwJHlrrc9Nhecc/qhXgYAwLXiSTQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlI4+1AvgyHLphy7P8We9ojrnsnNOP0irAQC4ZjyJBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKI3ouZOXNm/nRmnn+Qrv+kmXncwbh2a2ZePzOnHup1AAAcTo4+1Au4nvrfk/zrtdYHDvVCrq9m5qi11pWHeh0AAIeCJ9F7mJlnJPnSJC+bmSfMzG/NzPkz886Z+fZtzCNn5qUz8wcz84GZ+eGZ+bFtzHkzc6tt3KO2cy+emd+bmZvuZb47zswfzcyFM/OmmbnTftb24Jl513a9N5ZrOWl7f8nMvGRmPm+Pa3/OzDxnZn5+Zo6amadsa79kZv7tNua0mXndzPxOkktn5mYz84ptPe+amTP2se5Hz8wFM3PBlVdcfo2+FwCA6xMRvYe11mOS/EWSb0xysySvXWvdfXv/lJm52Tb0Lkm+N8k9kpyd5Iq11slJ3prk4duY319r3X2tdbckf5rkB/Yy5TOT/Mha65Qkj0vya/tZ3s8m+dbtevffdfxA1vLcJD+x1joxyaVJnrjr/KOTPD/Jn6+1fnpb5+Xbfd89yaNm5ku2sfdI8oS11lcl+d+S/MVa625rrbsk+aO9LXqt9cy11qlrrVOPuumx+7k9AIDDg+0c+3ffJPfftX/5xkluv71+3Vrrw0k+PDOXJ/mD7filSU7cXt9lZn4+yS2T3DzJK3dffGZunuTrkpw7M1cdPmY/63lzkmfPzIuS/P6u4/tdy8wcm+SWa603bMefk+TcXef/RpIXrbXO3nXfJ87Mg7b3xyY5IcnHkrx91zaXS5P80sz8YpKXr7XetJ+1AwDcYIjo/Zsk37nWeu+/ODjzNUk+uuvQJ3e9/2Q+/Xd9dpIHrLUunplHJjltj+t/TpK/X2uddCCLWWs9Zpv79CQXzcxV5x3IWvbnLUm+cWb+z7XWP2fnvn9krbVn9J+W5B93refPZ+aUJN+W5Bdm5lVrrZ87kHsBADic2c6xf69M8iOzPSaemZPL82+R5C9n5kZJHrLnh2utf0jygZl58Hb9mZm77etiM3PHtdbb1lo/m+Svk9zuQBax1ro8yd/NzNdvhx6W5A27hvyXJH+YnSfiR2fnvn9wW3dm5st3bWPZvZ4vys7Wkd9O8ktJvvpA1gMAcLjzJHr/npzkqUku2UL6siT3K87/mSRvS/LB7Gx9uMVexjwkya/PzE8nuVGSFyS5eB/Xe8rMnJCdJ8Wv2caddIBreUSSZ2w/bnx/ku/b/eFa65e3bR/P29Z0fJJ3bPf9P5M8YC/XvOu2pk8m+XiSHzzAtQAAHNZmrXWo18AR5JjjTljHPeKp1TmXnXP6wVkMAMB+zMyFa629/u9p2M4BAAAl2zmuh2bmCUkevMfhc3f9t2cAAHAIiejroS2WBTMAwPWU7RwAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQOvpQL4Ajy11ve2wuOOf0Q70MAIBrxZNoAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACgdfagXwJHl0g9dnuPPekV1zmXnnH6QVgMAcM14Eg0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURXZqZ42fmew/1Og6FI/neAQB2E9G945PsNSRn5ujP7lI+e7Z7Oz77uHcAgCOJiN7MzENn5u0zc9HM/MbMfM3MXDIzN56Zm83Mu2fmLknOSfL127jHzswjZ+bcmfmDJK/axv7WzJw/M++cmW/frv/ImXnpzPzBzHxgZn54Zn5sG3PezNxqG3fHmfmjmblwZt40M3faz5qfPTPP2Mb9+czcbzt+45l51sxcul3/G7fjb5uZO+86//Uzc8rVrPlT97aXe3/TzJy063pvnpkTr+OvBgDgeucG++S0MTNfmeSMJPdaa318Zn4tyVckeVmSn09ykyS/vdZ618ycleRxa62rgvWRSe6Z5MS11t/OzH9I8tq11vfPzC2TvH1m/nib6i5JTk5y4yT/T5KfWGudPDO/kuThSZ6a5JlJHrPWet/MfE2SX0tyn/0s//gk/2uSOyZ53cx8WZIfSpK11l23CH/VzHx5khck+a4kT5yZ45J80VrrwqtZ8+57O22Pe//bJI9M8u+26x+z1rqk+NMDAByWRPSOb0pySpLzZybZiea/SvJzSc5P8s9JztzP+a9ea/3t9vq+Se4/M4/b3t84ye23169ba304yYdn5vIkf7AdvzTJiTNz8yRfl+TcbR1JcszVrP1Fa61PJnnfzLw/yZ2S3DvJf0qStdafzcwHk3x5khcleXWSJ2Ynps89gDXvvrc9nZvkZ2bm8Um+P8mz9zZoZh6d5NFJctS/+oKruR0AgOs/Eb1jkjxnrfWT/+LgzP+S5OZJbpSdsPzHfZy/+/gk+c611nv3uNbXJPnorkOf3PX+k9n5Lj4nyd+vtU4q1r728n72OnCtD83M32xbLs5I8m8PYM37uuesta6YmVcn+fbsRPmp+xj3zOw8Yc8xx52w53oBAA479kTveE2SB83MFybJzNxqZu6QnfD7mSTPT/KL29gPJ7nFfq71yiQ/Mtuj5Jk5+UAXsdb6hyQfmJkHb+fOzNztak578Mx8zszcMcmXJnlvkjcmech2jS/PzlPlqwL5BUl+PMmxa61LyzXv7d5/M8nTk5y/nyfWAAA3KCI6yVrrPUl+Ojt7hy/JzpaHRyT5xFrrd7Lzg7q7z8x9klyS5BMzc/HMPHYvl3tydp5cXzIz79reNx6S5Adm5uIk787OU979eW+SNyT5v7Ozl/qfs7OP+qiZuTTJC5M8cq111VPvFyf57uxs7WjX/Bn3vta6MMk/JHlWd5sAAIevWcu/XT9czcyzk7x8rfXiQ7iGL0ry+iR32vZm79cxx52wjnvEU6s5Ljvn9Gu0NgCAa2NmLlxr7XW7qifRXGMz8/Akb0vyhAMJaACAGwo/LDwMzMwTkjx4j8PnrrUeeQiW8ylrrecmee6hXAMAwKEgog8Da62zk5x9qNcBAMAO2zkAAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKB09KFeAEeWu9722FxwzumHehkAANeKJ9EAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUDr6UC+AI8ulH7o8x5/1iuqcy845/SCtBgDgmvEkGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEpXG9Ezc/zMvOtgTD4zXzQzL95enzQz33YA55w2My/fz+dPmpnHXYs1Xavzr0sz81O7Xh+07+G6MjMfOdRrAAD4bDikT6LXWn+x1nrQ9vakJFcb0Tc0s2Nf38NP7eM4AACH0IFG9FEz83/NzLtn5lUzc5PtyfF5M3PJzLxkZj4vSWbmzJl5z3b8BduxJ83M82bmtTPzvpl51Hb8+Jl518x8bpKfS3LGzFw0M2fMzD1m5i0z887t/35FcV9323Oubb7Hz8z529r+/a7jT5iZ987MHyf5il3HP+Ne9mZf97evObf7/tOZ+bUk70hyu71c85wkN9n+Hs/f1/ewjd3Xd/H6mTl1e/35M3PZ9vrOM/P27dqXzMwJ2/GXzsyF2/UfvWstH5mZs2fm4m2e22zHv2Rm3rrd35P38/d59MxcMDMXXHnF5fsaBgBw2DjQiD4hya+ute6c5O+TfGeS5yb5ibXWiUkuTfLEbexZSU7ejj9m1zVOTHJ6knsm+dmZ+aKrPlhrfSzJzyZ54VrrpLXWC5P8WZJvWGudvH32H4r7+oy5Zua+233cIztPvU+ZmW+YmVOSfHeSk5N8R5K777rOvu7lWs25jf+KJM9da5281vrgnhdba52V5J+2v8dDtsN7+x6SfX8X+/KYJE9ba52U5NQk/+92/PvXWqdsx86cmVtvx2+W5Ly11t2SvDHJVf+Q8LQkv77WunuS/7GvydZaz1xrnbrWOvWomx57NUsDALj+O/oAx31grXXR9vrCJHdMcsu11hu2Y89Jcu72+pIkz5+ZlyZ56a5r/Ne11j8l+aeZeV12wvKi7NuxSZ6zPSVdSW50gGvd11z3TnLfJO/cxtw8O1F6iyQvWWtdkSQz87Jd19nXvVzbOf97kg+utc4r7in5zO/h+Jk5Nvv+LvblrUmeMDNfnOT311rv246fOTMP3F7fblvr3yT5WJKr9qFfmORbttf3yqdD/nlJfrG8HwCAw9KBPon+6K7XVya55X7Gnp7kV5OckuTCmbkq1Nce4/Z8v6cnJ3ndWusuSf5Nkhsf4Fr3Ndck+YXtye5Ja60vW2v9l6tZy77u5bqY8x8P+G4+bc/v4er+IegT+fR3/Km/31rrd5LcP8k/JXnlzNxnZk5L8s1J7rk9cX7nrnM+vta66v72nPfqvkcAgBuca/rDwsuT/N3MfP32/mFJ3rD9QO52a63XJfnx7MT2zbcx3z4zN962CJyW5Pw9rvnh7DwVvsqxST60vX5kub69zfXKJN8/MzdPkpm57cx8YXa2Jzxw2+d9i+wEe67mXq7tnAfq4zOz3yfwa629fhfb68uy8w8ASXLVDzgzM1+a5P1rracneVl2tqIcm+Tv1lpXzMydknztAazvzdnZCpMkD9nfQACAG5ID3c6xN49I8oyZuWmS9yf5viRHJfntbYvBJPmVtdbfz0ySvD3JK5LcPsmT11p/MTPH77re65KcNTMXJfmFJP8xO9s5fizJa8u1fcZcSf5iZr4yyVu39XwkyUPXWu+YmRdmZ2vJB5O8abvGXu/lupgzO09zD8Qzk1wyM+9I8oT9jNvbd5Ekv5TkRTPzsPzLv+EZSR46Mx/Pzl7mn8vOk/HHzMwlSd6b5EC2mvxokt+ZmR9N8nsHeE8AAIe9+fS/pT+Ik8w8KclH1lq/dNAnOwRu6Pd3XTrmuBPWcY94anXOZeecfnAWAwCwHzNz4Vrr1L195n+xEAAAStdmO8cBW2s96bq+5sx8X3a2E+z25rXWD13Xcx3sOWfmbUmO2ePww9Zal16b6wIAcHB8ViL6YFhrPSvJs24Ic661vua6viYAAAeP7RwAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQOvpQL4Ajy11ve2wuOOf0Q70MAIBrxZNoAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaD6rLv3Q5Tn+rFfk+LNecaiXAgBwjYloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJ6ENgZo6fme/d9f7UmXn6QZjnATPzVQcw7uiZ+euZ+YUDGPuYmXn4dbNCAIDDk4g+NI5P8qmIXmtdsNY68yDM84AkVxvRSe6b5L1JvmtmZn8D11rPWGs99zpYGwDAYUtEXwMz8/CZuWRmLp6Z583MHWbmNdux18zM7bdxz56Zp8/MW2bm/TPzoO0S5yT5+pm5aGYeOzOnzczLt3OeNDO/NTOv3845c9e8D52Zt2/n/cbMHLUd/8jMnL2t57yZuc3MfF2S+yd5yjb+jvu5pe9J8rQk/z3J1+6a75yZec92X7+0a32P214/ambO3+b9vZm56XX1NwYAuD4T0aWZuXOSJyS5z1rrbkl+NMl/TvLctdaJSZ6fZPfWjOOS3DvJ/bITz0lyVpI3rbVOWmv9yl6muVOSb01yjyRPnJkbzcxXJjkjyb3WWicluTLJQ7bxN0ty3raeNyZ51FrrLUleluTx2zz/bR/3c5Mk35Tk5Ul+NztBnZm5VZIHJrnzdl8/v5fTf3+tdfdt3j9N8gP7mOPRM3PBzFxw5RWX720IAMBhRUT37pPkxWutv06StdbfJrlnkt/ZPn9edqL5Ki9da31yrfWeJLc5wDlesdb66DbHX23nfVOSU5KcPzMXbe+/dBv/sexEcJJcmJ3tIgfqfklet9a6IsnvJXng9oT7H5L8c5LfnJnvSHLFXs69y8y8aWYuzU7Q33lvE6y1nrnWOnWtdepRNz22WBoAwPXT0Yd6AYehSbKuZszuzz+6x7kHYvc5V2bne5okz1lr/eRexn98rbX2GH+gvifJvWbmsu39rZN841rrj2fmHtmJ9e9O8sPZ+QeI3Z6d5AFrrYtn5pFJTivmBQA4bHkS3XtNdn6Ad+vkU9se3pKd0Ex2nsj+ydVc48NJbnEN5n3QzHzhVfPOzB2uzTwz86+y89T89mut49daxyf5oSTfMzM3T3LsWusPk/y7JCft5RK3SPKXM3OjfHprCQDADZ6ILq213p3k7CRvmJmLk/xykjOTfN/MXJLkYdnZJ70/lyT5xPaDvMce4LzvSfLTSV61zfPq7Oy33p8XJHn8zLxzHz8s/I4kr11r7X7y/V+z84PEWyd5+TbXG5LsbZ0/k+Rt21r+7EDuAwDghmA+vQsADr5jjjthHfeIpyZJLjvn9EO7GACA/ZiZC9dap+7tM0+iAQCg5IeFR4iZ+dUk99rj8NPWWs86FOsBADiciegjxFrrhw71GgAAbihs5wAAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAIDS0Yd6ARxZ7nrbY3PBOacf6mUAAFwrnkQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElE81l16Ycuz/FnveJQLwMA4FoR0QAAUBLRAABQEtEANUtPnAAAQkRJREFUAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0dfCzBw/M9+76/2pM/P0gzDPA2bmq65mzLNn5gMzc9H2n7dc1+sAAGCHiL52jk/yqYhea12w1jrzIMzzgCT7jejN49daJ23/+boDvfjs8P8LAAAH6IgOp5l5+MxcMjMXz8zzZuYOM/Oa7dhrZub227hnz8zTZ+YtM/P+mXnQdolzknz99uT3sTNz2sy8fDvnSTPzWzPz+u2cM3fN+9CZeft23m/MzFHb8Y/MzNnbes6bmdvMzNcluX+Sp2zj71je45Nm5nG73r9re4J+/Mz86cz8WpJ3JLndzDxl+/zSmTljG3/azLxxZl4yM++ZmWdcFdwzc9+ZeevMvGNmzp2Zm1/T7wIA4HByxEb0zNw5yROS3GetdbckP5rkPyd57lrrxCTPT7J7a8ZxSe6d5H7ZieckOSvJm7Ynv7+yl2nulORbk9wjyRNn5kYz85VJzkhyr7XWSUmuTPKQbfzNkpy3reeNSR611npLkpfl00+Z/9t+buuq0L5oZp5/AH+Gr9ju9+QkpyY5Kcndknzzdq3jtnH3SPJ/JLlrkjsm+Y6Z+fwkP53km9daX53kgiQ/trdJZubRM3PBzFxw5RWXH8CyAACu344+1As4hO6T5MVrrb9OkrXW387MPZN8x/b585L8x13jX7rW+mSS98zMbQ5wjlestT6a5KMz81dJbpPkm5KckuT8mUmSmyT5q238x5K8fHt9YZJvKe/p8WutFxfjP7jWOm97fe8kv7vWujLJ/zczb0hy9yT/kOTta633J8nM/O429p+zs8Xkzdt9fG6St+5tkrXWM5M8M0mOOe6EVd4TAMD1zpEc0ZPk6oJu9+cf3ePcA7H7nCuz8/eeJM9Za/3kXsZ/fK219hh/bX0i//LfONx41+t/3PV6f/e0599pbeNfvdb6nmu3PACAw88Ru50jyWuSfNfM3DpJZuZWSd6S5Lu3zx+S5E+u5hofTnKLazDvg2bmC6+ad2bucBDmucplSb56m+urk3zJPsa9MckZM3PUzHxBkm9I8vbts3vMzJdse6HPyM7f5bwk95qZL9uufdOZ+fJruEYAgMPKERvRa613Jzk7yRtm5uIkv5zkzCTfNzOXJHlYdvZJ788lST6x/RDwsQc473uys5f4Vds8r87Ofuv9eUGSx8/MO6/mh4W790RfNDOfm+T3ktxqZi5K8oNJ/nwf575ku5+Lk7w2yY+vtf7H9tlbs7MP/F1JPpDkJWut/5nkkUl+d7uP87KzBxwA4AZvPr17AD7TzJyW5HFrrftdF9c75rgT1nGPeGouO+f06+JyAAAHzcxcuNY6dW+fHbFPogEA4Jo6kn9YeFiamV9Ncq89Dj9trfWsgzHfWuv1SV5/MK4NAHC4EtGHmbXWDx3qNQAAHOls5wAAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKI/v/bu/uoz++6vvOv9waqNVUsCJGiEOBgERQQBqqAuxERa+sB7KKIEdabBVZZgxyN4mJXakvF5hwtWa2Lerg9UNqCsBzc5cbInaYxTCA3giCCbFdLi6iLIG6A5LN/XN+Bi+Gaua5XZiaTGR6Pc+Ywv+/d5/P9feeXPOeb7+8CAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoimpvV197pNvnAs//x6Z4GAMAJEdEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLR3Kyu+9OPnO4pAACcMBENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AAKVbdETPzPkz8z27Xh+amUtPwTiPnpl77bPN18/M783M1TPzBzPzzJs41pfOzA/ven3BzLymPMbtZ+aTM/PkmzKHYpzLT+XxAQDOVLfoiE5yfpJPR/Ra6/Ba66JTMM6jkxw3opO8MMmT1lr3S/I1Sf79TRzrS5P88H4b7eM7k1yR5HEneJw9zcw5SbLWevCpOD4AwJnulEb0zDxhZq6dmWtm5sUzc5eZuWxbdtnM3Hnb7gUzc+nMXD4z75+Zx2yHeHaSb9zu/j5t913bmXnmzDxvZt607XPRrnG/d2au3PZ77pEonJmPzcyztvlcMTPnzcyDkzwyySXb9nc/xuncIckHk2StdcNa613bMW87M6/azumKmbnPrvn9+K45/f7MnL+d0923sS7ZVv+dmXn5zLx7Zl4yM7PPW/u4JD+W5Ctm5k67xvjYzPz8zFw1M781Mw/a9f48ctvmnJm5ZGbets35ydvyC2bmjTPz0iTXHTnermP/xMxct713z96WPXE7zjUz84qZ+aJ95g0AcFY4ZRE9M/dO8owkD1tr3TfJU5P8UpIXrbXuk+QlSXY/mnHHJA9N8u3ZCc0keXqSt6617rfW+sU9hrlnkm9N8qAkPzMzt56Zr07y2CQP2e4a35Dkwm37c5Ncsc3nLUmeuNa6PMmrk1y8jfO+Y5zSLyZ5z8y8cmaePDNfuC3/Z0nesZ3T/5LkRfu8NU9P8r5trIu3ZV+X5Eezczf8bkkecqydZ+Yrk3z5WuvK7NwNf+yu1ecmedNa6wFJPprkXyT5liTfkeRnt21+MMlH1loPTPLAJE+cmbtu6x6U5Blrrc+6Kz8z35adu/X/YHvv/tW26jfWWg/clv3Bduy95vykmTk8M4dv+PhHjv3OAACcIU7lneiHJXn5WuvDSbLW+osk35Dkpdv6F2cnmo941Vrrxu0O73kHHOM311rXb2N8aNvvm5M8IMnbZubq7fXdtu0/keTI88dXZedxkQNZa/1skkNJXp+dR0xeu6166HYuWWv9dpLbzcxtDnrczZVrrT9Za92Y5Op95vXd+cyjJC/LZz/S8Yld87ouyZvXWp/cfn/kmI9I8oTtvfm9JLdLco9d8/jjPcZ8eJLnr7U+nnz6WibJ18zMW2fmuuz8ReXee014rfWra61Da61D53xR+9YAANzy3OoUHnuSrH222b3++qP2PYjd+9yQnfOZJC9ca/3UHtt/cq21jtr+wLa71L8yM7+W5M9m5nbHmOtK8ql89l9SvnCP7Y7Y6zyO5XFJzpuZI3fX/97M3GOt9d589vndeOS4a60bZ+bIMSfJj6y1Xrf7oDNzQZK/PsaYx7qWL0jy6LXWNTPzfUkuOM68AQDOGqfyTvRlSb5rC83MzG2TXJ6dO6nJzp3L39nnGB9N8sU3YdzHzMwdjow7M3c50XFm5h/velb5HtmJ3f83O4+FXLhtc0GSD6+1/irJB5Lcf1t+/yRHHpm4Ked0ZA5/P8m5a607rbXOX2udn+Tn8pn39CBel+SHZubW2zG/ambO3Wef1yf5gSPPPG/XMtk5jw9ux7rwWDsDAJxtTllEr7XemeRZSd48M9ck+YUkFyX5/pm5Nsnjs/Oc9PFcm+RT2xfXnnbAcd+V5KeTvH4b5w3Zed76eF6W5OKZecdxvlj4+Ow8E311dh7fuHCtdUOSZyY5tI317CT/w7b9K5Lcdtv+h5L84Ta/P0/yu9sXDS9J53FJXnnUslek+ykdv57kXUnePjO/n+S52eeO/Frrtdl5bvzwdj5HvjD5T7PzSMgbkry7mAMAwBltPvNf/+HU+4I73mNd/8H3nu5pAADsa2auWmsd2mvdLf3nRAMAwC3Oqfxi4RlpZn45n/sj5p6z1nr+zTyPV+Yzz1Ef8ZNHfyEQAICbn4g+ylrrKad7Dkmy1vqO0z0HAAD25nEOAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloblZfe6fbnO4pAACcMBENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURzc3quj/9yOmeAgDACRPRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAULrFR/TMnD8z37Pr9aGZufQUjPPombnXPtu8YGb+dGa+YHv9ZTPzgXKcL52ZH971+oKZeU15jNvPzCdn5snNfq2ZufxUHh8A4Ex1i4/oJOcn+XREr7UOr7UuOgXjPDrJcSN6c0OSH7gpA8zMOUm+NMkP77Ppfr4zyRVJHneCx9nTNs+stR58Ko4PAHCmO+URPTNPmJlrZ+aamXnxzNxlZi7bll02M3fetnvBzFw6M5fPzPtn5jHbIZ6d5Btn5uqZedruO7cz88yZed7MvGnb56Jd437vzFy57ffcI2E4Mx+bmWdt87liZs6bmQcneWSSS7bt736cU/rXSZ42M7c66jxnZi6Zmd+fmetm5rHb8gtm5o0z89Ik123nc/dtnEu23f/OzLx8Zt49My+ZmdnnbX1ckh9L8hUzc6ddc/jYzPz8zFw1M781Mw/a9d48ctvmnG2eb9uuwZOPMc/MzMd2HfsntvO6ZmaevS174naca2bmFTPzRfvMGwDgrHBKI3pm7p3kGUketta6b5KnJvmlJC9aa90nyUuS7H40445JHprk27MTm0ny9CRvXWvdb631i3sMc88k35rkQUl+ZmZuPTNfneSxSR6y1rpfdu4eX7htf26SK7b5vCXJE9dalyd5dZKLt3Hed5zT+k9JfifJ449a/k+S3C/JfZM8PDtBfsdt3YOSPGOtda/tfN63jXPxtv7rkvxodu6E3y3JQ441+Mx8ZZIvX2tdmeTfb+d5xLlJ3rTWekCSjyb5F0m+Jcl3JPnZbZsfTPKRtdYDkzwwyRNn5q57zHP3mN+WnTv1/2B73/7Vtuo31loP3Jb9wXbsveb8pJk5PDOHb/j4R451agAAZ4xTfSf6YUlevtb6cJKstf4iyTckeem2/sXZieYjXrXWunGt9a4k5x1wjN9ca12/jfGhbb9vTvKAJG+bmau313fbtv9EkiPPIF+VncdFWv8yycX57PfvoUn+7VrrhrXWf03y5uxEapJcudb64+Mc78q11p+stW5McvU+c/ru7MRzkrwsn/1IxyeSvHb7/XVJ3rzW+uT2+yPHfESSJ2zvy+8luV2Se+wzz4cnef5a6+PJp69jknzNzLx1Zq7Lzl9S7r3XhNdav7rWOrTWOnTOF93mOKcGAHBmuNX+m5yQSbL22Wb3+uuP2vcgdu9zQ3bOaZK8cK31U3ts/8m11jpq+8pa64+2CP2uXYuPN9+/3ueQe53DsTwuyXkzc+TO+t+bmXustd6bzz63G48cd611467HTybJj6y1Xrf7oDNzwXHmeazr+IIkj15rXTMz35fkguPMGwDgrHGq70RfluS7ZuZ2STIzt01yeXbupiY7dy9/Z59jfDTJF9+EcR8zM3c4Mu7M3OUkj/OsJD++6/Vbkjx2e+b49kn+2yRXnoRxPm1m/n6Sc9dad1prnb/WOj/Jz+Uz7+dBvC7JD83MrbdjftXMnLvPPq9P8gNHnnnermOycx4f3I514bF2BgA425zSiF5rvTM7sfnmmbkmyS8kuSjJ98/Mtdl5rvip+xzm2iSf2r689rQDjvuuJD+d5PXbOG/IzvPWx/OyJBfPzDv2+WLhkTHemeTtuxa9cpvrNUl+O8lPrLX+yx77/XmS392+gHjJ0ev38bhtnN1eke6ndPx6knclefvM/H6S52afu/Frrddm55nxw9sd+CN/efin2Xkk5A1J3l3MAQDgjDaf+a//cOp9wR3vsa7/4HtP9zQAAPY1M1ettQ7tte5M+DnRAABwi3Kqv1h4RpqZX87n/pi556y1nn8zzuGVSe561OKfPPoLgQAA3PxE9B7WWk+5BczhO073HAAA2JvHOQAAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkogEAoCSiAQCgJKIBAKAkorlZfe2dbnO6pwAAcMJENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQAAJRENAAAlEQ0AACURDQ3q+v+9COnewoAACdMRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUQDAEBJRAMAQElEAwBASUSfAjNz/sx8z67Xh2bm0lMwzqNn5l4H2O5WM/Phmfm5o5ZfMjPvnJlL9tjnkTPz9JM5XwCAs4WIPjXOT/LpiF5rHV5rXXQKxnl0kn0jOskjkrwnyXfNzOxa/uQk919rXbx745m51Vrr1WutZ5+0mQIAnEVE9B5m5gkzc+3MXDMzL56Zu8zMZduyy2bmztt2L5iZS2fm8pl5/8w8ZjvEs5N848xcPTNPm5kLZuY12z7PnJnnzcybtn0u2jXu987Mldt+z52Zc7blH5uZZ23zuWJmzpuZByd5ZJJLtu3vfpxTelyS5yT5T0m+fjvmq5Ocm+T3Zuax27n8wsy8McnPz8z3zcwvbdueNzOv3Ma/Zhs7M/Oqmblqu5v9pJN2AQAAbuFE9FFm5t5JnpHkYWut+yZ5apJfSvKitdZ9krwkye5HM+6Y5KFJvj078ZwkT0/y1rXW/dZav7jHMPdM8q1JHpTkZ2bm1jPz1Ukem+Qha637JbkhyYXb9ucmuWKbz1uSPHGtdXmSVye5eBvnfcc4n7+d5JuTvCbJv81OUGet9cgkf7Pt+++2zb8qycPXWj921GEuTfLmbfz7J3nntvwH1loPSHIoyUUzc7tjzOFJM3N4Zg7f8PGP7LUJAMAZRUR/roclefla68NJstb6iyTfkOSl2/oXZyeaj3jVWuvGtda7kpx3wDF+c611/TbGh7b9vjnJA5K8bWau3l7fbdv+E9mJ4CS5KjuPixzUtyd541rr40lekeQ7jtzh3sN/WGvdsMfyhyX5lSRZa92w1jpSwhfNzDVJrkjylUnusddB11q/utY6tNY6dM4X3aaYOgDALdOtTvcEboEmydpnm93rrz9q34PYvc8N2bkOk+SFa62f2mP7T6611lHbH9TjkjxkZj6wvb5dkm9K8lt7bPvXBz3ozFyQ5OFJvmGt9fGZeVOSLyzmBQBwxnIn+nNdlp0v4N0uSWbmtkkuT/Ld2/oLk/zOPsf4aJIvvgnjPmZm7nBk3Jm5y4mMMzNfkp275ndea52/1jo/yVOyPdJRzu2HtmOesx33Nkn+cgvoe2Z71hoA4POBiD7KWuudSZ6V5M3bowq/kOSiJN8/M9cmeXx2npM+nmuTfGr7Et7TDjjuu5L8dJLXb+O8ITvPWx/Py5JcPDPvOMYXC/9Jkt9ea+2+8/1/JHnkzHzBQea1eWqSb5qZ67LzOMm9k7w2ya22uf7z7DzSAQDweWE+85QAnHpfcMd7rOs/+N7TPQ0AgH3NzFVrrUN7rXMnGgAASr5YeJaYmV9O8pCjFj9nrfX80zEfAICzmYg+S6y1nnK65wAA8PnC4xwAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtEAAFAS0QAAUBLRAABQEtHcrL72Trc53VMAADhhIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIhoAAEoiGgAASiIaAABKIvoAZub8mfmeXa8Pzcylp2CcR8/MvfbZZmbmp2fmvTPzhzPzxpm598mey3HG//X95ggAcLYT0QdzfpJPR/Ra6/Ba66JTMM6jk+wXqE9J8uAk911rfVWSn0vy6pn5wlMwn8+x1vof11rvujnGAgC4pfq8iOiZecLMXDsz18zMi2fmLjNz2bbsspm587bdC2bm0pm5fGbePzOP2Q7x7CTfODNXz8zTZuaCmXnNts8zZ+Z5M/OmbZ+Ldo37vTNz5bbfc2fmnG35x2bmWdt8rpiZ82bmwUkemeSSbfu7H+N0fjLJj6y1Pp4ka63XJ7k8yYXbsf/hzLx9O/Zl27Jztzm+bWbeMTOP2pafPzNv3bZ/+zaHbOf3ppl5+cy8e2ZeMjOzrXvTzBzafv8rM3N4Zt45M//sZF0vAIBburM+ordHHZ6R5GFrrfsmeWqSX0ryorXWfZK8JMnuRzPumOShSb49O/GcJE9P8ta11v3WWr+4xzD3TPKtSR6U5Gdm5tYz89VJHpvkIWut+yW5IVvoJjk3yRXbfN6S5IlrrcuTvDrJxds479vjXL4kybl7rDuc5N4zc/skv5bkv9+O/Z3b+mck+e211gOTfFN2Qv3cJB9K8i1rrftvc939Pnxdkh/Nzp3xuyV5yB7n/Yy11qEk90ny383MffbYJjPzpC22D//Zn/3ZXpsAAJxRzvqITvKwJC9fa304SdZaf5HkG5K8dFv/4uxE8xGvWmvduD2ycN4Bx/jNtdb12xgf2vb75iQPSPK2mbl6e323bftPJHnN9vursvO4yImYJCvJ1yd5y1rrj5NPn2uSPCLJ07d5vCnJFya5c5JbJ/m1mbkuyX/IZz9KcuVa60/WWjcmufoYc/yumXl7knckuXeO8SjKWutX11qH1lqHbn/725/AaQIA3DLc6nRP4GZwJDCPZ/f664/a9yB273NDdt7XSfLCtdZP7bH9J9da66jt97XW+quZ+euZudta6/27Vt0/yZtz7HOd7Nydfs9nLZx5ZpL/muS+2fkL1f+3zznt3veuSX48yQPXWn85My/ITpwDAJz1Ph/uRF+WnTumt0uSmbltdp4h/u5t/YVJfmefY3w0yRffhHEfMzN3ODLuzNzlJIxzSZJLZ+Zvb8d9eHbupL80yX/MzmMVdz0y5rbP65L8yK7nmr9uW36bJB/c7jY/Psk5xfl9SZK/TvKRmTkvybcV+wIAnNHO+jvRa613zsyzkrx5Zm7IzqMHFyV53sxcnOTPknz/Poe5NsmnZuaaJC/YjrHfuO+amZ9O8vqZ+W+SfDI7P1nj/z7Obi/LzuMVFyV5zF7PRSf535L83STXbefzX5I8aq31N0n+ZmaelOQ3tjE/lORbkvzzJP86ybVbSH8gO898/5skr5iZ70zyxuxE8YGsta6ZmXckeWeS9yf53YPuCwBwppvPPFUAp96hQ4fW4cOHT/c0AAD2NTNXbT9E4XN8PjzOAQAAJ9VZ/zjHmWpmfjmf+2PlnrPWev7pmA8AAJ8hom+h1lpPOd1zAABgbx7nAACAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAICSiAYAgJKIBgCAkogGAIDSrLVO9xz4PDIzH03yntM9j89zX5bkw6d7ErgOtwCuwennGpx+rsHx3WWtdfu9Vtzq5p4Jn/fes9Y6dLon8flsZg67Bqef63D6uQann2tw+rkGN53HOQAAoCSiAQCgJKK5uf3q6Z4ArsEthOtw+rkGp59rcPq5BjeRLxYCAEDJnWgAACiJaE6amfmHM/OemfmjmXn6HutnZi7d1l87M/c/6L4czAlegw/MzHUzc/XMHL55Z372OMA1uOfM/MeZuX5mfrzZl4M5wWvgc3CSHOA6XLj9c+jambl8Zu570H05mBO8Bj4L+1lr+eXXCf9Kck6S9yW5W5K/leSaJPc6apt/lOT/SjJJvj7J7x10X79O7TXY1n0gyZed7vM4k38d8BrcIckDkzwryY83+/p1aq/Bts7n4Oa7Dg9O8ne333+bfyfccq7B9tpnYZ9f7kRzsjwoyR+ttd6/1vpEkpcledRR2zwqyYvWjiuSfOnM3PGA+7K/E7kGnBz7XoO11ofWWm9L8sl2Xw7kRK4BJ89BrsPla62/3F5ekeQrDrovB3Ii14ADENGcLHdK8v/sev0n27KDbHOQfdnfiVyDJFlJXj8zV83Mk07ZLM9uJ/Jn2efg5DjR99Hn4ORor8MPZue/kt2UfdnbiVyDxGdhX/4fCzlZZo9lR//ol2Ntc5B92d+JXIMkecha6z/PzB2SvGFm3r3WestJneHZ70T+LPscnBwn+j76HJwcB74OM/NN2Qm4h7b7clwncg0Sn4V9uRPNyfInSb5y1+uvSPKfD7jNQfZlfydyDbLWOvK/H0ryyuz8p0A6J/Jn2efg5Dih99Hn4KQ50HWYmfsk+fUkj1pr/XmzL/s6kWvgs3AAIpqT5W1J7jEzd52Zv5Xku5O8+qhtXp3kCdtPiPj6JB9Za33wgPuyv5t8DWbm3Jn54iSZmXOTPCLJ79+ckz9LnMifZZ+Dk+Mmv48+ByfVvtdhZu6c5DeSPH6t9YfNvhzITb4GPgsH43EOToq11qdm5n9O8rrsfCP4eWutd87M/7St/9+T/J/Z+ekQf5Tk40m+/3j7nobTOKOdyDVIcl6SV85MsvPPhZeutV57M5/CGe8g12BmvjzJ4SRfkuTGmfnR7Hxj/q98Dk7ciVyDJF8Wn4OT4oD/PPpfk9wuyb/Z3vNPrbUO+XfCyXEi1yD+nXAg/h8LAQCg5HEOAAAoiWgAACiJaAAAKIloAAAoiWgAACiJaAAAKIloAAAoiWgAACj9/1V2awCcvcW2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting feature importances\n",
    "features = sorted(zip(X.columns, clf.feature_importances_), key = lambda x: x[1])\n",
    "cols = [f[0] for f in features]\n",
    "width = [f[1] for f in features]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches(10,200)\n",
    "plt.margins(y=0.001)\n",
    "\n",
    "ax.barh(y=cols, width=width)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7495874-3809-4136-ab62-ca60304f8a54",
   "metadata": {},
   "source": [
    "# <font color='red'>Regression Models</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537ec754",
   "metadata": {},
   "source": [
    "## Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e3c1da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing dependancies for Random Forest Regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#setting up random forest regression model and fitting the model to our training data\n",
    "regressor = RandomForestRegressor(n_estimators=100, random_state=1)\n",
    "RFR_Results = {}\n",
    "Mean_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a8886503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9412595682846915\n",
      "Training Score: 0.9907343072614215\n",
      "Testing Score: 0.9412595682846915\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    regressor.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    RFR_R2 = r2_score(y_test, y_pred)\n",
    "    RFR_Results[f\"Test_{str(i)}\"] = RFR_R2\n",
    "    print(f\"Test_{str(i)}: {RFR_R2}\")\n",
    "    print(f\"Training Score: {regressor.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {regressor.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "46507de9-4ab7-4dfc-9726-8043c861de8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "Mean_Results[\"RFR_Mean\"] = mean(RFR_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a87f4e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting y_pred vs y_test\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_pred)\n",
    "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "#regression line\n",
    "ax.plot(y_test, LinearRegression().fit(y_test, y_pred).predict(y_test))\n",
    "ax.set_title('R2: ' + str(r2_score(y_test, y_pred)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc5b2d5",
   "metadata": {},
   "source": [
    "### Gradient Boosting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b0d4bfd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets, ensemble\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9b9aaf7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "GBR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d6c83956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9370151612565313\n",
      "Test_1: 0.9370860356250177\n",
      "Test_2: 0.9370694643166642\n",
      "Test_3: 0.9371113307199004\n",
      "Test_4: 0.9370939401402107\n"
     ]
    }
   ],
   "source": [
    "#tuning n_estimators\n",
    "for i in range(5):\n",
    "    params = {\n",
    "    \"n_estimators\": 40 + i*2,\n",
    "    \"max_depth\": 10,\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"loss\": \"squared_error\",\n",
    "    \"random_state\": 1,\n",
    "    }\n",
    "    regressor2 = ensemble.GradientBoostingRegressor(**params)\n",
    "    regressor2.fit(X_train, y_train.values.ravel())\n",
    "    y_pred2 = regressor2.predict(X_test)\n",
    "    GBR_R2 = r2_score(y_test, y_pred2)\n",
    "    print(f\"Test_{str(i)}: {GBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e7a35513-7f59-4504-b185-d842a7296f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.921876549276938\n",
      "Test_1: 0.9328650172774868\n",
      "Test_2: 0.9405700337592888\n",
      "Test_3: 0.9397480449589538\n",
      "Test_4: 0.9400466699924872\n"
     ]
    }
   ],
   "source": [
    "#tuning max_depth\n",
    "for i in range(5):\n",
    "    params = {\n",
    "    \"n_estimators\": 46,\n",
    "    \"max_depth\": 5 + i,\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"loss\": \"squared_error\",\n",
    "    \"random_state\": 1,\n",
    "    }\n",
    "    regressor2 = ensemble.GradientBoostingRegressor(**params)\n",
    "    regressor2.fit(X_train, y_train.values.ravel())\n",
    "    y_pred2 = regressor2.predict(X_test)\n",
    "    GBR_R2 = r2_score(y_test, y_pred2)\n",
    "    print(f\"Test_{str(i)}: {GBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9dc773d2-01fe-4948-bad5-2598c96c2bd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9405700337592888\n",
      "Training Score: 0.9798978586240277\n",
      "Testing Score: 0.9405700337592888\n",
      "Test_1: 0.9405700337592888\n",
      "Training Score: 0.9798978586240277\n",
      "Testing Score: 0.9405700337592888\n",
      "Test_2: 0.9405700337592888\n",
      "Training Score: 0.9798978586240277\n",
      "Testing Score: 0.9405700337592888\n",
      "Test_3: 0.9405700337592888\n",
      "Training Score: 0.9798978586240277\n",
      "Testing Score: 0.9405700337592888\n",
      "Test_4: 0.9405700337592888\n",
      "Training Score: 0.9798978586240277\n",
      "Testing Score: 0.9405700337592888\n"
     ]
    }
   ],
   "source": [
    "#tuned result\n",
    "for i in range(5):\n",
    "    params = {\n",
    "    \"n_estimators\": 46,\n",
    "    \"max_depth\": 7,\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"loss\": \"squared_error\",\n",
    "    \"random_state\": 1,\n",
    "    }\n",
    "    regressor2 = ensemble.GradientBoostingRegressor(**params)\n",
    "    regressor2.fit(X_train, y_train.values.ravel())\n",
    "    y_pred2 = regressor2.predict(X_test)\n",
    "    GBR_R2 = r2_score(y_test, y_pred2)\n",
    "    GBR_Results[f\"Test_{str(i)}\"] = GBR_R2\n",
    "    print(f\"Test_{str(i)}: {GBR_R2}\")\n",
    "    print(f\"Training Score: {regressor2.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {regressor2.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3a2afb8e-0107-47e9-b047-0b1e62420eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"GBR_Mean\"] = mean(GBR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a52cc5-ba34-4f65-9ed6-449fb7b51dc4",
   "metadata": {},
   "source": [
    "### Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "593f764b-b31a-4e8d-aff1-7c81233f20fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DTR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "78af4ac1-e923-4b6c-8e2d-af155bef0dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8948517741762633\n",
      "Test_1: 0.8997753745574174\n",
      "Test_2: 0.8996402763407082\n",
      "Test_3: 0.9085586835702346\n",
      "Test_4: 0.89578710115919\n",
      "Test_5: 0.9032562904021911\n",
      "Test_6: 0.8967957259396024\n",
      "Test_7: 0.9027409506282325\n",
      "Test_8: 0.9017909169400319\n",
      "Test_9: 0.8897646330306459\n"
     ]
    }
   ],
   "source": [
    "#tuning max_depth\n",
    "for i in range(10):\n",
    "    max_depth = 10 + i\n",
    "    regressor3 = DecisionTreeRegressor(max_depth=max_depth, random_state=1)\n",
    "    regressor3.fit(X_train, y_train.values.ravel())\n",
    "    y_pred3 = regressor3.predict(X_test)\n",
    "    DTR_R2 = r2_score(y_test, y_pred3)\n",
    "    print(f\"Test_{str(i)}: {DTR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "439ca954-7ddb-44cd-95f8-0e7ecbec31f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8932655059379832\n",
      "Test_1: 0.9032562904021911\n",
      "Test_2: 0.9113248582474307\n",
      "Test_3: 0.9051090208376009\n",
      "Test_4: 0.9034025498466609\n"
     ]
    }
   ],
   "source": [
    "#tuning random_state\n",
    "for i in range(5):\n",
    "    random_state = 0\n",
    "    regressor3 = DecisionTreeRegressor(max_depth=15, random_state= i + 0)\n",
    "    regressor3.fit(X_train, y_train.values.ravel())\n",
    "    y_pred3 = regressor3.predict(X_test)\n",
    "    DTR_R2 = r2_score(y_test, y_pred3)\n",
    "    print(f\"Test_{str(i)}: {DTR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9ca487c-2257-4499-8ee9-e1233513a911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9113248582474307\n",
      "Training Score: 0.9951122504972344\n",
      "Testing Score: 0.9113248582474307\n"
     ]
    }
   ],
   "source": [
    "#tuned result\n",
    "for i in range(1):\n",
    "    regressor3 = DecisionTreeRegressor(max_depth=15, random_state= 2)\n",
    "    regressor3.fit(X_train, y_train.values.ravel())\n",
    "    y_pred3 = regressor3.predict(X_test)\n",
    "    DTR_R2 = r2_score(y_test, y_pred3)\n",
    "    DTR_Results[f\"Test_{str(i)}\"] = DTR_R2\n",
    "    print(f\"Test_{str(i)}: {DTR_R2}\")\n",
    "    print(f\"Training Score: {regressor3.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {regressor3.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "08353a15-4016-47f9-99f4-1977506d6ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"DTR_Mean\"] = mean(DTR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8585473f-84db-4f79-8ad2-83828a1faec5",
   "metadata": {},
   "source": [
    "### Extreme Gradient Boosting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7c0dec94-a0d9-45f1-8287-378074143ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "XGB_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c8f00a62-83a2-4680-b78c-5e53cbf7ef80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: -0.10557063653671461\n",
      "Test_1: 0.9263726968967119\n",
      "Test_2: 0.929012990318077\n",
      "Test_3: 0.9276007555146111\n",
      "Test_4: 0.922154351025094\n"
     ]
    }
   ],
   "source": [
    "#tuning learning rate\n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0 + i/10, max_delta_step=0, max_depth=22, min_child_weight=1, n_estimators=500,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "651851e6-4224-4050-b42b-784ca0e145a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.929012990318077\n",
      "Test_1: -0.08531664223951485\n",
      "Test_2: -0.06618425905730674\n",
      "Test_3: -0.047915868352235824\n",
      "Test_4: -0.030462557821288927\n"
     ]
    }
   ],
   "source": [
    "#tuning max_delta_step\n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0 + i/10, max_depth=22, min_child_weight=1, n_estimators=500,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "78b2206d-8a24-4877-90be-26d1cecc233e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.7941383946124553\n",
      "Test_1: 0.9329849516436486\n",
      "Test_2: 0.9320869671679608\n",
      "Test_3: 0.9376143195973604\n",
      "Test_4: 0.9357436089621549\n"
     ]
    }
   ],
   "source": [
    "#tuning max_depth\n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=1 + i*2, min_child_weight=0.1, n_estimators=500,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f532bc83-ee6c-4e3e-9972-4f107e6a1b45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9376143195973604\n",
      "Test_1: 0.9351101754991102\n",
      "Test_2: 0.9298498629935733\n",
      "Test_3: 0.9404070045704949\n",
      "Test_4: 0.9375432600028635\n"
     ]
    }
   ],
   "source": [
    "#tuning min_child_weight\n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=1+ i, n_estimators=500,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "22f1017a-26e4-4531-9894-afef327d17be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9444621472645238\n",
      "Test_1: 0.9436393219902186\n",
      "Test_2: 0.94210088047293\n",
      "Test_3: 0.9414520637674305\n",
      "Test_4: 0.9404070045704949\n"
     ]
    }
   ],
   "source": [
    "#tuning n_estimators\n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=100 + i * 100,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fdcb8380-7256-4f89-8e02-96f2d84203a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.944771325258664\n",
      "Test_1: 0.9447718071383524\n",
      "Test_2: 0.9453927134290394\n",
      "Test_3: 0.9453782212793974\n",
      "Test_4: 0.9453423829018334\n",
      "Test_5: 0.945235997678285\n",
      "Test_6: 0.9455590225830355\n",
      "Test_7: 0.9453390507047184\n",
      "Test_8: 0.9454040318270388\n",
      "Test_9: 0.9455413093082286\n",
      "Test_10: 0.945557077957654\n",
      "Test_11: 0.9453692740027753\n",
      "Test_12: 0.9453745824778803\n",
      "Test_13: 0.945279686137345\n",
      "Test_14: 0.9452345069462251\n",
      "Test_15: 0.9451561814811802\n",
      "Test_16: 0.945032713742022\n",
      "Test_17: 0.9450497586133829\n",
      "Test_18: 0.945021459616011\n",
      "Test_19: 0.9450163085711166\n"
     ]
    }
   ],
   "source": [
    "#tuning n_estimators\n",
    "for i in range(20):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=50 + i,tree_method='hist', random_state=1)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "eb98365d-32aa-472b-a8ff-0d412ab96842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.945557077957654\n",
      "Training Score: 0.9740780811748032\n",
      "Testing Score: 0.945557077957654\n",
      "Test_1: 0.945557077957654\n",
      "Training Score: 0.9740780811748032\n",
      "Testing Score: 0.945557077957654\n",
      "Test_2: 0.945557077957654\n",
      "Training Score: 0.9740780811748032\n",
      "Testing Score: 0.945557077957654\n",
      "Test_3: 0.945557077957654\n",
      "Training Score: 0.9740780811748032\n",
      "Testing Score: 0.945557077957654\n",
      "Test_4: 0.945557077957654\n",
      "Training Score: 0.9740780811748032\n",
      "Testing Score: 0.945557077957654\n"
     ]
    }
   ],
   "source": [
    "#tuned results \n",
    "for i in range(5):\n",
    "    Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)\n",
    "    Regressor4.fit(X_train, y_train.values.ravel())\n",
    "    y_pred4 = Regressor4.predict(X_test)\n",
    "    XGB_R2 = r2_score(y_test, y_pred4)\n",
    "    XGB_Results[f\"Test_{str(i)}\"] = XGB_R2\n",
    "    print(f\"Test_{str(i)}: {XGB_R2}\")\n",
    "    print(f\"Training Score: {Regressor4.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor4.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e65952f1-5c3a-4094-afae-22ae72cd1c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"XGB_Mean\"] = mean(XGB_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe49eadc-ee6a-475e-8f20-4d2bee7a40dd",
   "metadata": {},
   "source": [
    "### Histogram-based Gradient Boosting Regression Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6a4a8186-3f41-4ebc-ac56-606946bc4b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "HGBR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fbcf4de6-0e1a-4597-9049-2cbf3fdecf1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9402202525212427\n",
      "Test_1: 0.9385928898128992\n",
      "Test_2: 0.9389272242583997\n",
      "Test_3: 0.9395015587869717\n",
      "Test_4: 0.9340308236043885\n"
     ]
    }
   ],
   "source": [
    "#tuning learning rate\n",
    "for i in range(5):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1 + i/10,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "666d2eaa-f62f-4f54-80ea-07b675724a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.7842329294711563\n",
      "Test_1: 0.9402202525212427\n",
      "Test_2: 0.9402202525212427\n",
      "Test_3: 0.9402202525212427\n",
      "Test_4: 0.9402202525212427\n"
     ]
    }
   ],
   "source": [
    "#tuning max_iter ... default is best\n",
    "for i in range(5):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_iter= 10 + i*100,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "42188560-da41-4516-b3f6-51b393e0dea2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9417282681458561\n",
      "Test_1: 0.9413724726749249\n",
      "Test_2: 0.9428392809969187\n",
      "Test_3: 0.942827778116123\n",
      "Test_4: 0.9421645746188598\n"
     ]
    }
   ],
   "source": [
    "#tuning max_leaf_nodes\n",
    "for i in range(5):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=78+i,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e2ef4566-ab9d-46bb-a6f3-ef826a6438b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9354437822140499\n",
      "Test_1: 0.9416633572786589\n",
      "Test_2: 0.9426905036317689\n",
      "Test_3: 0.9428392809969187\n",
      "Test_4: 0.9428392809969187\n"
     ]
    }
   ],
   "source": [
    "#tuning max_depth\n",
    "for i in range(5):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=5 + i*5,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d66d5e14-e7fb-4ef0-8ed0-390bae69ce25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9393586832419345\n",
      "Test_1: 0.9420883216673592\n",
      "Test_2: 0.9420567699751853\n",
      "Test_3: 0.9418934393685874\n",
      "Test_4: 0.9432399964576289\n",
      "Test_5: 0.9428392809969187\n",
      "Test_6: 0.938872500810657\n",
      "Test_7: 0.939483776010592\n",
      "Test_8: 0.9382551259630177\n",
      "Test_9: 0.9395534507707536\n",
      "Test_10: 0.9399401900996123\n",
      "Test_11: 0.9387897121168817\n",
      "Test_12: 0.9395912952265543\n",
      "Test_13: 0.9391155112114671\n",
      "Test_14: 0.9398509196943307\n"
     ]
    }
   ],
   "source": [
    "#tuning min_samples_leaf\n",
    "for i in range(15):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=15 + i,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "061ebd8d-066d-4e6a-a546-e5cc9d4addc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9432399964576289\n",
      "Training Score: 0.9501400408544759\n",
      "Testing Score: 0.9432399964576289\n"
     ]
    }
   ],
   "source": [
    "#tuned result\n",
    "for i in range(1):\n",
    "    Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)\n",
    "    Regressor5.fit(X_train, y_train.values.ravel())\n",
    "    y_pred5 = Regressor5.predict(X_test)\n",
    "    HGBR_R2 = r2_score(y_test, y_pred5)\n",
    "    HGBR_Results[f\"Test_{str(i)}\"] = HGBR_R2\n",
    "    print(f\"Test_{str(i)}: {HGBR_R2}\")\n",
    "    print(f\"Training Score: {Regressor5.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor5.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2740a85d-28c5-4128-97a2-3abc5cb00745",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"HGBR_Mean\"] = mean(HGBR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6057af-de0d-4866-a777-2b2d8bd20307",
   "metadata": {},
   "source": [
    "### Regression based on k-nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d6c069b-1c42-4020-a72d-976004646a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "KNN_Results = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a1e290b0-020b-40db-b9c1-01a2056017e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8977783369661396\n",
      "Test_1: 0.8997382733445937\n",
      "Test_2: 0.9004015153982655\n",
      "Test_3: 0.8997078128271808\n",
      "Test_4: 0.8985256568716811\n",
      "Test_5: 0.8983790953757459\n",
      "Test_6: 0.8971340922061329\n",
      "Test_7: 0.8970864300367696\n",
      "Test_8: 0.8954829292855824\n",
      "Test_9: 0.8951244793517042\n"
     ]
    }
   ],
   "source": [
    "#tuning n_neighbors\n",
    "for i in range(10):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=5+i, weights='distance', leaf_size=50)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9ca18ef7-4dac-454a-b7a7-81b67e0cc4ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9004015876649516\n"
     ]
    }
   ],
   "source": [
    "#testing ball_tree algo\n",
    "for i in range(1):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=7, algorithm='ball_tree',weights='distance', leaf_size=50)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "583ae8f1-e311-42a8-bbf3-505a0f207af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9004015876649516\n"
     ]
    }
   ],
   "source": [
    "#testing kd_tree algo\n",
    "for i in range(1):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=50)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c6eb0757-032f-41f2-9c27-8df716905f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9004015876649516\n"
     ]
    }
   ],
   "source": [
    "#augmenting leaf_size\n",
    "for i in range(1):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "10b8b31e-3ef8-4424-a9d4-788d7b71b657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8998572532005106\n",
      "Test_1: 0.9008856161173948\n",
      "Test_2: 0.900574541777975\n",
      "Test_3: 0.9003758506779931\n",
      "Test_4: 0.9010790965844405\n",
      "Test_5: 0.9011497291106677\n",
      "Test_6: 0.9011631142908456\n",
      "Test_7: 0.9010082080418809\n",
      "Test_8: 0.9005728209919608\n",
      "Test_9: 0.9007208016764341\n"
     ]
    }
   ],
   "source": [
    "#tuning p\n",
    "for i in range(10):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1+i/10)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "197248f3-5145-46d0-be3f-815d3ed52716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9011631142908456\n",
      "Training Score: 1.0\n",
      "Testing Score: 0.9011631142908456\n"
     ]
    }
   ],
   "source": [
    "#tuned result\n",
    "for i in range(1):\n",
    "    Regressor6 = KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)\n",
    "    Regressor6.fit(X_train, y_train.values.ravel())\n",
    "    y_pred6 = Regressor6.predict(X_test)\n",
    "    KNN_R2 = r2_score(y_test, y_pred6)\n",
    "    KNN_Results[f\"Test_{str(i)}\"] = KNN_R2\n",
    "    print(f\"Test_{str(i)}: {KNN_R2}\")\n",
    "    print(f\"Training Score: {Regressor6.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor6.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6d321356-e005-49e9-b188-1739d9451d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"KNN_Mean\"] = mean(KNN_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1f2649-d32f-49fc-ac37-75bef229997b",
   "metadata": {},
   "source": [
    "### Lasso linear model with iterative fitting along a regularization path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "15ec815a-b3b4-4839-81fa-3b61ade69d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "LSSO_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "15a91d12-0fcd-4223-bc3b-c9ba123825cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.16403721502628743\n",
      "Training Score: 0.1831025475759055\n",
      "Testing Score: 0.16403721502628743\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    Regressor7 = LassoCV(n_alphas=10 + i*5, random_state=1)\n",
    "    Regressor7.fit(X_train, y_train.values.ravel())\n",
    "    y_pred7 = Regressor7.predict(X_test)\n",
    "    LSSO_R2 = r2_score(y_test, y_pred7)\n",
    "    LSSO_Results[f\"Test_{str(i)}\"] = LSSO_R2\n",
    "    print(f\"Test_{str(i)}: {LSSO_R2}\")\n",
    "    print(f\"Training Score: {Regressor7.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor7.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6c1719b2-a7a9-4b3c-b36d-4011ebdca99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"LSSO_Mean\"] = mean(LSSO_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b803bd9-fd4c-4d08-9f28-19c1707eb920",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fc8f8d3e-0713-458b-89d9-cb7486c3d107",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "SGDR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "de687bf6-4084-4092-ad42-610df62d3aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.6658753808369796\n",
      "Training Score: 0.6926150070373176\n",
      "Testing Score: 0.6658753808369796\n",
      "Test_1: 0.6620369669800579\n",
      "Training Score: 0.6909134868478758\n",
      "Testing Score: 0.6620369669800579\n",
      "Test_2: 0.6674472598199748\n",
      "Training Score: 0.6936848745110136\n",
      "Testing Score: 0.6674472598199748\n",
      "Test_3: 0.6732694655330287\n",
      "Training Score: 0.6949809102870099\n",
      "Testing Score: 0.6732694655330287\n",
      "Test_4: 0.6573579068893882\n",
      "Training Score: 0.6876536794643544\n",
      "Testing Score: 0.6573579068893882\n",
      "Test_5: 0.6725463353479293\n",
      "Training Score: 0.6949974232549072\n",
      "Testing Score: 0.6725463353479293\n",
      "Test_6: 0.6673287294162564\n",
      "Training Score: 0.6939518936605086\n",
      "Testing Score: 0.6673287294162564\n",
      "Test_7: 0.669729838318311\n",
      "Training Score: 0.6942294544348121\n",
      "Testing Score: 0.669729838318311\n",
      "Test_8: 0.6723008719969642\n",
      "Training Score: 0.6935043473900835\n",
      "Testing Score: 0.6723008719969642\n",
      "Test_9: 0.671318164925536\n",
      "Training Score: 0.6868225245454335\n",
      "Testing Score: 0.671318164925536\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    Regressor8 = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))\n",
    "    Regressor8.fit(X_train, y_train.values.ravel())\n",
    "    y_pred8 = Regressor8.predict(X_test)\n",
    "    SGDR_R2 = r2_score(y_test, y_pred8)\n",
    "    SGDR_Results[f\"Test_{str(i)}\"] = SGDR_R2\n",
    "    print(f\"Test_{str(i)}: {SGDR_R2}\")\n",
    "    print(f\"Training Score: {Regressor8.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor8.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d7b1a4b1-c3a2-4456-a878-d0e7b90a92bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"SGDR_Mean\"] = mean(SGDR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887e3ce8-5bd6-4b15-8b4a-27f3c3c44cce",
   "metadata": {},
   "source": [
    "### Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea676856-5134-43ce-98cf-66ed9d36e939",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "BRR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c6928b1e-1f13-460a-a804-461db26503ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_1: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_2: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_3: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_4: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_5: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_6: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_7: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_8: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n",
      "Test_9: 0.6725693025873284\n",
      "Training Score: 0.6959035752061936\n",
      "Testing Score: 0.6725693025873284\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    Regressor11 = BayesianRidge(compute_score=True)\n",
    "    Regressor11.fit(X_train, y_train.values.ravel())\n",
    "    y_pred11 = Regressor11.predict(X_test)\n",
    "    BRR_R2 = r2_score(y_test, y_pred11)\n",
    "    BRR_Results[f\"Test_{str(i)}\"] = BRR_R2\n",
    "    print(f\"Test_{str(i)}: {BRR_R2}\")\n",
    "    print(f\"Training Score: {Regressor11.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor11.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cb30efac-b8bc-463b-a500-a971f0fa4bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"BRR_Mean\"] = mean(BRR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4eed868-c0c4-4b24-b704-01c6ed46c990",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "13d7ffa4-a744-41c1-8ad7-28c8654f2db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "SVR_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0705645f-acba-440a-b0e4-7d4960975c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.1892589341401063\n",
      "Training Score: 0.19476005825378095\n",
      "Testing Score: 0.1892589341401063\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    Regressor12 = SVR()\n",
    "    Regressor12.fit(X_train, y_train.values.ravel())\n",
    "    y_pred12 = Regressor12.predict(X_test)\n",
    "    SVR_R2 = r2_score(y_test, y_pred12)\n",
    "    SVR_Results[f\"Test_{str(i)}\"] = SVR_R2\n",
    "    print(f\"Test_{str(i)}: {SVR_R2}\")\n",
    "    print(f\"Training Score: {Regressor12.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {Regressor12.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f56ac049-686d-47f9-a7be-6c511031f601",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"SVR_Mean\"] = mean(SVR_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c648453f-32d9-426e-8f33-e0cb8071f6c6",
   "metadata": {},
   "source": [
    "# <font color='red'>Individual Regression Model Results</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a367b53e-2f67-493c-9ce9-bb4ee3cc5307",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df = pd.DataFrame.from_dict(Mean_Results, orient='index', columns=['R2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2a593e39-8641-443d-b262-5d03804949c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGB_Mean</th>\n",
       "      <td>0.945557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGBR_Mean</th>\n",
       "      <td>0.943240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFR_Mean</th>\n",
       "      <td>0.941260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR_Mean</th>\n",
       "      <td>0.940570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR_Mean</th>\n",
       "      <td>0.911325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN_Mean</th>\n",
       "      <td>0.901163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRR_Mean</th>\n",
       "      <td>0.672569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDR_Mean</th>\n",
       "      <td>0.667921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR_Mean</th>\n",
       "      <td>0.189259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSSO_Mean</th>\n",
       "      <td>0.164037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R2\n",
       "XGB_Mean   0.945557\n",
       "HGBR_Mean  0.943240\n",
       "RFR_Mean   0.941260\n",
       "GBR_Mean   0.940570\n",
       "DTR_Mean   0.911325\n",
       "KNN_Mean   0.901163\n",
       "BRR_Mean   0.672569\n",
       "SGDR_Mean  0.667921\n",
       "SVR_Mean   0.189259\n",
       "LSSO_Mean  0.164037"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean_Results_df.sort_values(by=\"R2\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026fcaa5-44ba-4b2c-b5e8-17a2af579a73",
   "metadata": {},
   "source": [
    "# <font color='yellow'><b>Extreme Gradient Boosting Regression for the Win! R2 = 94.5557</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc83b67c-ae59-4216-a800-153bd185505c",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23000407-d320-4cc9-a3fd-7f0f2a44f0bb",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7c139d-6fd2-4cf1-92b1-558d7445fe7b",
   "metadata": {},
   "source": [
    "## <font color='red'>BOOOOOOOSTING w/ ADABOOST Regression!</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a138a0f7",
   "metadata": {},
   "source": [
    "### Random Forest Regression with AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bb86d58b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "RFRwAB_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "39e0ca58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9380303736771411\n",
      "Test_1: 0.9334578469907417\n"
     ]
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    model =  AdaBoostRegressor(regressor)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred2 = model.predict(X_test)\n",
    "    R2 = r2_score(y_test, y_pred2)\n",
    "    RFRwAB_Results[f\"Test_{str(i)}\"] = R2\n",
    "    print(f\"Test_{str(i)}: {R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "df879aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RFRwAB_Mean\"] = mean(RFRwAB_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e048332",
   "metadata": {},
   "source": [
    "### Extreme Gradient Boosting Regression with AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4256ba57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating \"regressor3\" by feeding the Gradient Boosting Regressor into the AdaBoostRegressor\n",
    "Regressor4 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "720772e5-dbda-4cb8-b178-2ecb9e07eaf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9398385840834341\n",
      "Test_1: 0.9428173869505413\n"
     ]
    }
   ],
   "source": [
    "XGB_Boosted_Results = {}\n",
    "\n",
    "for i in range(2):\n",
    "    regressor13 =  AdaBoostRegressor(Regressor4)\n",
    "    regressor13.fit(X_train, y_train.values.ravel())\n",
    "    y_pred13 = regressor13.predict(X_test)\n",
    "    XGB_Boosted_R2 = r2_score(y_test, y_pred13)\n",
    "    XGB_Boosted_Results[f\"Test_{str(i)}\"] = XGB_Boosted_R2\n",
    "    print(f\"Test_{str(i)}: {XGB_Boosted_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "38973494",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"XGB_Boosted_Mean\"] = mean(XGB_Boosted_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44945c70-3f5b-4f19-a201-82a45f44bcf6",
   "metadata": {},
   "source": [
    "### Histogram-based Gradient Boosting Regression Tree with AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "1a8ba878-7ffd-43f7-84bd-4d7a79dd2a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "Regressor5 = HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)\n",
    "HGBR_Boosted_Results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5688a63-cc4c-4e33-80c3-27aa09f202b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2):\n",
    "    regressor14 =  AdaBoostRegressor(Regressor5)\n",
    "    regressor14.fit(X_train, y_train.values.ravel())\n",
    "    y_pred14 = regressor13.predict(X_test)\n",
    "    HGBR_Boosted_R2 = r2_score(y_test, y_pred14)\n",
    "    HGBR_Boosted_Results[f\"Test_{str(i)}\"] = HGBR_Boosted_R2\n",
    "    print(f\"Test_{str(i)}: {HGBR_Boosted_R2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00dd1ec6-1945-4f91-9a20-6397b7b3c33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"HGBR_Boosted_Mean\"] = mean(HGBR_Boosted_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3903e12a-9a91-4cc0-b88d-80aa74870661",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df = pd.DataFrame.from_dict(Mean_Results, orient='index', columns=['R2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4dfb788-1a2a-41ca-8bfc-a7d70ccc25f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df.sort_values(by=\"R2\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359b3941-eb7b-41c3-9f16-c7ed05cc14f4",
   "metadata": {},
   "source": [
    "# <font color='cyan'>AdaBoost isn't Boosting!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa34df88-ba33-48ab-8f2a-4233b08be33f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "09fe0eff-47a6-4d73-8617-eebd34f4f8df",
   "metadata": {},
   "source": [
    "# <font color='red'>Stacking Regressors</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32285eb-985b-4439-b591-92de012dc728",
   "metadata": {},
   "source": [
    "### Attempt #1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4f09157c-e9d4-46ac-bf79-6ff9e66b90a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e5397348-1607-43f6-90bf-89b10985b278",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack_Results = {}\n",
    "\n",
    "# define the base models\n",
    "M0 = list()\n",
    "M0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "M0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "M0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "M0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "M0.append(('xgb', XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)))\n",
    "# define meta learner model\n",
    "M1 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "193be56e-f79d-43e5-b078-5918d324e158",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9452372201509847\n",
      "Training Score: 0.9703391731259962\n",
      "Testing Score: 0.9452372201509847\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=M0, final_estimator=M1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack_Results[f\"Test_{str(i)}\"] = Tuned_Stack_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1450a0da-900d-42d1-9106-fba11d0eab49",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack_Mean\"] = mean(Tuned_Stack_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f574c42-3b31-45c7-a568-085180a1dfce",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eab3b620-50ed-4473-8567-dee72d1b491e",
   "metadata": {},
   "source": [
    "### Attempt #2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3aef1cf7-9e66-49df-a1a6-700fe76f8681",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack2_Results = {}\n",
    "\n",
    "# define the base models\n",
    "N0 = list()\n",
    "N0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "N0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "N0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "N0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "N0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "# define meta learner model\n",
    "N1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a01faf11-8ab0-45de-9e13-6e22735c7f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9302341631022117\n",
      "Training Score: 0.9588679848769095\n",
      "Testing Score: 0.9302341631022117\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=N0, final_estimator=N1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack2_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack2_Results[f\"Test_{str(i)}\"] = Tuned_Stack2_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack2_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1de907fa-6aa8-4143-862a-bd0ce5207ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack2_Mean\"] = mean(Tuned_Stack2_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1395269-056b-4805-bd46-124e52425412",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4acf6544-596e-4b07-9d15-3ff9b7078208",
   "metadata": {},
   "source": [
    "### Attempt #3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "86de8940-54e0-489f-a644-4e8093493649",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack3_Results = {}\n",
    "\n",
    "# define the base models\n",
    "O0 = list()\n",
    "O0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "O0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "O0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "O0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "O0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "O1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c5d0b162-88ff-49eb-9d06-31425bf284cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9302264249170573\n",
      "Training Score: 0.9588403173757827\n",
      "Testing Score: 0.9302264249170573\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=O0, final_estimator=O1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack3_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack3_Results[f\"Test_{str(i)}\"] = Tuned_Stack3_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack3_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "01de9358-0824-491c-a45b-4ccd11f30a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack3_Mean\"] = mean(Tuned_Stack2_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57f68224-acc6-4f53-8c45-d60db8be10e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e3ee7fb-a29e-4aac-80d2-198a30e3c790",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e0d7491a-66ae-489b-80f5-a900dd378018",
   "metadata": {},
   "source": [
    "### Attempt #4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5edaa6eb-65d2-4138-9475-0c8363ff42c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack4_Results = {}\n",
    "\n",
    "# define the base models\n",
    "P0 = list()\n",
    "P0.append(('xgb', XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)))\n",
    "P0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "P0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "P0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "P1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2734cd2f-4a28-456a-9948-dedf97c2adc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9397625975414843\n",
      "Training Score: 0.9624080826860719\n",
      "Testing Score: 0.9397625975414843\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=P0, final_estimator=P1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack4_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack4_Results[f\"Test_{str(i)}\"] = Tuned_Stack4_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack4_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7e0a3a50-0ede-43ae-b8a2-1b5f3501c4f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack4_Mean\"] = mean(Tuned_Stack4_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9542c8da-fed1-4058-8b29-8b212abbf9b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e31d5b95-f9b7-49e8-9b5e-a23d83b6ff3f",
   "metadata": {},
   "source": [
    "### Attempt #5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c0e481a2-eb72-45f3-8832-be210f14ee29",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack5_Results = {}\n",
    "\n",
    "# define the base models\n",
    "Q0 = list()\n",
    "Q0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "Q0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "Q1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "5d910f30-ca44-4be9-a52b-c8b4bc4868ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9339559989954607\n",
      "Training Score: 0.9586426559139877\n",
      "Testing Score: 0.9339559989954607\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=Q0, final_estimator=Q1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack5_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack5_Results[f\"Test_{str(i)}\"] = Tuned_Stack5_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack5_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "daee692a-99e3-4235-bb95-213fd704940b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack5_Mean\"] = mean(Tuned_Stack5_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b283ee42-9e36-4b7c-9d1a-92f0922caaa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4234921-1efd-460d-b8d2-3f699fcc0607",
   "metadata": {},
   "source": [
    "### Attempt #6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bd9a537e-530b-4612-8fa5-1067abc30d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack6_Results = {}\n",
    "\n",
    "# define the base models\n",
    "R0 = list()\n",
    "R0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "R1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0831bdf5-6cda-4bb7-a926-03a51dc740bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9370742471215023\n",
      "Training Score: 0.9352189974208354\n",
      "Testing Score: 0.9370742471215023\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=R0, final_estimator=R1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack6_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack6_Results[f\"Test_{str(i)}\"] = Tuned_Stack6_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack6_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f73ca17d-549e-4431-bf39-4cb3bcf7cb61",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack6_Mean\"] = mean(Tuned_Stack6_Results.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5560887f-3f0d-4ec2-be09-4154065a8581",
   "metadata": {},
   "source": [
    "### Attempt #7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "890a65ec-9be1-4c49-9c24-476380f3e678",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack7_Results = {}\n",
    "\n",
    "# define the base models\n",
    "S0 = list()\n",
    "S0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "# define meta learner model\n",
    "S1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e151983d-485f-47ec-a3ab-d0a0207129b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9284470034985866\n",
      "Training Score: 0.9730772714566577\n",
      "Testing Score: 0.9284470034985866\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=S0, final_estimator=S1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack7_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack7_Results[f\"Test_{str(i)}\"] = Tuned_Stack7_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack7_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9881915b-ceee-42cf-86c1-a2889c63fb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack7_Mean\"] = mean(Tuned_Stack7_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e119d5a-b3ab-46f7-98d9-11217910b001",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "256f78fa-1063-412d-b1d8-495c981baa2f",
   "metadata": {},
   "source": [
    "### Attempt #8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a76c7cd0-d6e1-4447-889c-230bbaabfded",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack8_Results = {}\n",
    "\n",
    "# define the base models\n",
    "T0 = list()\n",
    "T0.append(('rfr', XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)))\n",
    "# define meta learner model\n",
    "T1 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6fbbaea1-e7fa-4118-ab74-126781a8312c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9133241815359668\n",
      "Training Score: 0.9334735659087902\n",
      "Testing Score: 0.9133241815359668\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=T0, final_estimator=T1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack8_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack8_Results[f\"Test_{str(i)}\"] = Tuned_Stack8_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack8_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "019249f4-f17c-4835-ae83-7a155818c77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack8_Mean\"] = mean(Tuned_Stack8_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f47593ba-1911-4218-bb4f-3fcfc901246a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1d4c2994-85e7-40a7-a549-741606388ea9",
   "metadata": {},
   "source": [
    "### Attempt #9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "bab6d877-19a4-4c09-9d2b-62a966a7b261",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack9_Results = {}\n",
    "\n",
    "# define the base models\n",
    "U0 = list()\n",
    "U0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "U0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#P0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "U1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "945cc193-45c5-4e97-b38c-0ea54e7773fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.920671787506839\n",
      "Training Score: 0.9762995202145586\n",
      "Testing Score: 0.920671787506839\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=U0, final_estimator=U1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack9_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack9_Results[f\"Test_{str(i)}\"] = Tuned_Stack9_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack9_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "694d7893-b416-49c1-aaa8-4a6a030d1a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack9_Mean\"] = mean(Tuned_Stack9_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5737ffc-64bd-4747-bfe1-6050bb9a7dcd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bd67fcad-b52a-4223-86c1-993337af06da",
   "metadata": {},
   "source": [
    "### Attempt #10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "8f8ab9bf-7c66-4ac5-b2c1-ffc475bcd003",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuned_Stack10_Results = {}\n",
    "\n",
    "# define the base models\n",
    "V0 = list()\n",
    "V0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#V0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "V0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "V1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "75d5d07d-31cd-4dac-abaa-b5d7c317748f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9226538241844429\n",
      "Training Score: 0.9649405221218686\n",
      "Testing Score: 0.9226538241844429\n",
      "Test_1: 0.9226538241844429\n",
      "Training Score: 0.9649405221218686\n",
      "Testing Score: 0.9226538241844429\n",
      "Test_2: 0.9226538241844429\n",
      "Training Score: 0.9649405221218686\n",
      "Testing Score: 0.9226538241844429\n",
      "Test_3: 0.9226538241844429\n",
      "Training Score: 0.9649405221218686\n",
      "Testing Score: 0.9226538241844429\n",
      "Test_4: 0.9226538241844429\n",
      "Training Score: 0.9649405221218686\n",
      "Testing Score: 0.9226538241844429\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=V0, final_estimator=V1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    Tuned_Stack10_R2 = r2_score(y_test, y_pred)\n",
    "    Tuned_Stack10_Results[f\"Test_{str(i)}\"] = Tuned_Stack10_R2\n",
    "    print(f\"Test_{str(i)}: {Tuned_Stack10_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "cda4fb93-62d3-4034-9fc2-e6236bdc2332",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"Tuned_Stack10_Mean\"] = mean(Tuned_Stack10_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8712e7a4-c620-4a3c-8516-f86bc9a721de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "dccfe58d-082f-4da0-905d-30c7c5018ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df = pd.DataFrame.from_dict(Mean_Results, orient='index', columns=['R2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3aeba963-8c9f-4629-beee-15b95dcfe35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XGB_Mean</th>\n",
       "      <td>0.945557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack_Mean</th>\n",
       "      <td>0.945237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGBR_Mean</th>\n",
       "      <td>0.943240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFR_Mean</th>\n",
       "      <td>0.941260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR_Mean</th>\n",
       "      <td>0.940570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack4_Mean</th>\n",
       "      <td>0.939763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack6_Mean</th>\n",
       "      <td>0.937074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack5_Mean</th>\n",
       "      <td>0.933956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack2_Mean</th>\n",
       "      <td>0.930234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack3_Mean</th>\n",
       "      <td>0.930234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack7_Mean</th>\n",
       "      <td>0.928447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack10_Mean</th>\n",
       "      <td>0.922654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack9_Mean</th>\n",
       "      <td>0.920672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned_Stack8_Mean</th>\n",
       "      <td>0.913324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR_Mean</th>\n",
       "      <td>0.911325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN_Mean</th>\n",
       "      <td>0.901163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRR_Mean</th>\n",
       "      <td>0.672569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDR_Mean</th>\n",
       "      <td>0.667921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR_Mean</th>\n",
       "      <td>0.189259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSSO_Mean</th>\n",
       "      <td>0.164037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          R2\n",
       "XGB_Mean            0.945557\n",
       "Tuned_Stack_Mean    0.945237\n",
       "HGBR_Mean           0.943240\n",
       "RFR_Mean            0.941260\n",
       "GBR_Mean            0.940570\n",
       "Tuned_Stack4_Mean   0.939763\n",
       "Tuned_Stack6_Mean   0.937074\n",
       "Tuned_Stack5_Mean   0.933956\n",
       "Tuned_Stack2_Mean   0.930234\n",
       "Tuned_Stack3_Mean   0.930234\n",
       "Tuned_Stack7_Mean   0.928447\n",
       "Tuned_Stack10_Mean  0.922654\n",
       "Tuned_Stack9_Mean   0.920672\n",
       "Tuned_Stack8_Mean   0.913324\n",
       "DTR_Mean            0.911325\n",
       "KNN_Mean            0.901163\n",
       "BRR_Mean            0.672569\n",
       "SGDR_Mean           0.667921\n",
       "SVR_Mean            0.189259\n",
       "LSSO_Mean           0.164037"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean_Results_df.sort_values(by=\"R2\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cfff91dd-e5d8-4ef0-be0e-1a6d74522ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7a9e87-9e15-449d-b455-d0abe57881c8",
   "metadata": {},
   "source": [
    "## <font color='red'>Does stacking any 1 regressor improve Extreme Gradient Boosting?</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5476ba5a-f243-4fd1-8e33-98ef4390ea37",
   "metadata": {},
   "source": [
    "#### RandomForestRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e3043fb2-60af-4b3a-ab5d-96409ba444d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "S1_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "W0 = list()\n",
    "W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#V0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#W0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "W1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2a9315d6-288b-410e-88e8-31aaa7e46f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9284470034985866\n",
      "Training Score: 0.9730772714566577\n",
      "Testing Score: 0.9284470034985866\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=W0, final_estimator=W1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S1_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S1_XGB_Results[f\"Test_{str(i)}\"] = S1_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S1_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "42c52b27-b309-44f4-9a4c-2704627aa1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S1_XGB_Mean\"] = mean(S1_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bbff6d3-2d9b-4bf2-bb49-6dd114336db0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9f1e0d76-ec45-46ca-991a-e9fb28372821",
   "metadata": {},
   "source": [
    "#### KNeighborsRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f555f97c-db51-48d0-8162-b39706bd4804",
   "metadata": {},
   "outputs": [],
   "source": [
    "S2_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "X0 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#W0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "X1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a9b1dc0d-6539-402d-bc2a-c42e9400c79c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8875004286754045\n",
      "Training Score: 0.9762943428250108\n",
      "Testing Score: 0.8875004286754045\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=X0, final_estimator=X1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S2_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S2_XGB_Results[f\"Test_{str(i)}\"] = S2_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S2_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8681074b-c2bc-4659-9ea3-2401e049274d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S2_XGB_Mean\"] = mean(S2_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4149e34-6f95-4679-a22d-dc7d8cea0320",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "38521edf-804e-4431-8907-8063b26e78de",
   "metadata": {},
   "source": [
    "#### LassoCV + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0648d0cf-61e7-418e-8061-343ac86ac6b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "S3_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "Y0 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "Y1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "06aea955-ab03-4ecc-9fe5-42e3698f3c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.48946265627302576\n",
      "Training Score: 0.4923885139905927\n",
      "Testing Score: 0.48946265627302576\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=Y0, final_estimator=Y1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S3_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S3_XGB_Results[f\"Test_{str(i)}\"] = S3_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S3_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cac5caa7-2318-41e3-b2fa-956fa0bc1ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S3_XGB_Mean\"] = mean(S3_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb5fe754-58d4-4a87-94b2-32bb1948993d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "53d6ea8a-b2ad-474d-afac-d21f13ca709e",
   "metadata": {},
   "source": [
    "#### DecisionTreeRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fcfb2266-2b40-49c3-839b-a8aaf47ffc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "S4_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "Z0 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "Z1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "edeaaa43-14c4-47ed-a57f-1a1c3819bcb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9040083887773951\n",
      "Training Score: 0.9721124334890292\n",
      "Testing Score: 0.9040083887773951\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=Z0, final_estimator=Z1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S4_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S4_XGB_Results[f\"Test_{str(i)}\"] = S4_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S4_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d5530f67-dde4-436f-b6b1-db8f6fa9a430",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S4_XGB_Mean\"] = mean(S4_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92ca6c6d-5f74-457f-92c6-95794032cb1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1784f296-bdda-47c7-8de5-cf23c6135d78",
   "metadata": {},
   "source": [
    "#### HistGradientBoostingRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7adf7a61-051a-461a-83a2-0d00d2ebf103",
   "metadata": {},
   "outputs": [],
   "source": [
    "S5_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AA0 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AA1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0ad4901e-cbab-43f0-b33a-eacde492d366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9370742471215023\n",
      "Training Score: 0.9352189974208354\n",
      "Testing Score: 0.9370742471215023\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AA0, final_estimator=AA1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S5_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S5_XGB_Results[f\"Test_{str(i)}\"] = S5_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S5_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cf6806f7-702a-4623-ae90-fa1fe531d110",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S5_XGB_Mean\"] = mean(S5_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e75e8d8-d6e4-40ad-bf8d-ff57614d723d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8c246ed4-2e9b-41a3-b4ef-3313a9d0bc2f",
   "metadata": {},
   "source": [
    "#### SVR + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f8149193-2d01-46a3-8d93-d07f6ce78c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "S6_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AB0 = list()\n",
    "AB0.append(('svr', SVR()))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AB1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c71ca30b-0a8d-4d92-bc4f-226c3ea73d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.3892693524334484\n",
      "Training Score: 0.41741164202330727\n",
      "Testing Score: 0.3892693524334484\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AB0, final_estimator=AB1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S6_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S6_XGB_Results[f\"Test_{str(i)}\"] = S6_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S6_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2f52b8f2-6d86-4383-99fd-232493c115f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S6_XGB_Mean\"] = mean(S6_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70bb0e89-00d7-46e1-9a2a-6b5b1127f844",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df534b4f-1f4d-4f6b-a3b7-ab15227fd1da",
   "metadata": {},
   "source": [
    "#### BayesianRidge + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ffb9bdf7-da92-4ccf-88a2-e42a7ffde883",
   "metadata": {},
   "outputs": [],
   "source": [
    "S7_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AC0 = list()\n",
    "AC0.append(('brr', BayesianRidge(compute_score=True)))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AC1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "57eeccbd-908e-4de0-a195-598c0ba2eb2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.7338796503284131\n",
      "Training Score: 0.7295432262199624\n",
      "Testing Score: 0.7338796503284131\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AC0, final_estimator=AC1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S7_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S7_XGB_Results[f\"Test_{str(i)}\"] = S7_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S7_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "92fc45c6-1077-4569-bb44-c737c3b2fffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S7_XGB_Mean\"] = mean(S7_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc78a9e8-fbde-4040-a0d5-bd487c4d03af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d6d2fe0f-5f8e-4b71-aabf-fefb887acaf3",
   "metadata": {},
   "source": [
    "#### SGDRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d644acc4-8074-48eb-acff-930a95b2137c",
   "metadata": {},
   "outputs": [],
   "source": [
    "S8_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AD0 = list()\n",
    "AD0.append(('sgd', make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AD1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f065ffd0-b474-4c7f-b0a4-f8c84c2db49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.7259225046995161\n",
      "Training Score: 0.7254024625514767\n",
      "Testing Score: 0.7259225046995161\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AD0, final_estimator=AD1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S8_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S8_XGB_Results[f\"Test_{str(i)}\"] = S8_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S8_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "27a38951-db54-4042-aa9c-7a88e7d2910c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S8_XGB_Mean\"] = mean(S8_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad019f2-4bba-4203-afeb-1b821e2a306b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b951c571-5e45-41b1-a1a7-dbef0b58849c",
   "metadata": {},
   "source": [
    "#### GradientBoostingRegressor + XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ab111ab8-71b4-49c0-be9c-8db3e41f7703",
   "metadata": {},
   "outputs": [],
   "source": [
    "S9_XGB_Results = {}\n",
    "params = {\n",
    "\"n_estimators\": 46,\n",
    "\"max_depth\": 7,\n",
    "\"learning_rate\": 0.1,\n",
    "\"loss\": \"squared_error\",\n",
    "\"random_state\": 1,}\n",
    "\n",
    "# define the base models\n",
    "AE0 = list()\n",
    "AE0.append(('sgd', ensemble.GradientBoostingRegressor(**params)))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AE1 = XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6a75c0e2-21b5-4e8c-b71a-9f9cdfb7db56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.926266533108794\n",
      "Training Score: 0.9611155146270429\n",
      "Testing Score: 0.926266533108794\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AE0, final_estimator=AE1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    S9_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    S9_XGB_Results[f\"Test_{str(i)}\"] = S9_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {S9_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f9bfbac0-84a1-4336-ba39-254d5a99b2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"S9_XGB_Mean\"] = mean(S9_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "debf788d-597b-46eb-a74f-37383d2aa777",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df = pd.DataFrame.from_dict(Mean_Results, orient='index', columns=['R2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "20c3eb84-30cb-4541-bd9c-99682bd4e86f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S5_XGB_Mean</th>\n",
       "      <td>0.937074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1_XGB_Mean</th>\n",
       "      <td>0.928447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S9_XGB_Mean</th>\n",
       "      <td>0.926267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4_XGB_Mean</th>\n",
       "      <td>0.904008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2_XGB_Mean</th>\n",
       "      <td>0.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S7_XGB_Mean</th>\n",
       "      <td>0.733880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S8_XGB_Mean</th>\n",
       "      <td>0.725923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3_XGB_Mean</th>\n",
       "      <td>0.489463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6_XGB_Mean</th>\n",
       "      <td>0.389269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   R2\n",
       "S5_XGB_Mean  0.937074\n",
       "S1_XGB_Mean  0.928447\n",
       "S9_XGB_Mean  0.926267\n",
       "S4_XGB_Mean  0.904008\n",
       "S2_XGB_Mean  0.887500\n",
       "S7_XGB_Mean  0.733880\n",
       "S8_XGB_Mean  0.725923\n",
       "S3_XGB_Mean  0.489463\n",
       "S6_XGB_Mean  0.389269"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean_Results_df.sort_values(by=\"R2\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2962b61-6d6e-4d75-be47-862272a5fc9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5190f1ea-62f8-4548-a047-bc4c2029a90d",
   "metadata": {},
   "source": [
    "## <font color='red'>Does stacking any 1 regressor improve Random Forest?</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6580fa6e-bd45-41b6-b376-4f189b0f45d9",
   "metadata": {},
   "source": [
    "#### XGBRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4c2e3f36-1762-41a6-91e9-b49273c806a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF1_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "W10 = list()\n",
    "W10.append(('xgbr', XGBRegressor(learning_rate=0.2, max_delta_step=0, max_depth=7, min_child_weight=4, n_estimators=60,tree_method='hist', random_state=0)))\n",
    "#V0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#W0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "W11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "528cbfec-4e9b-4811-b76c-834f07d4d1c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.9133241815359668\n",
      "Training Score: 0.9334735659087902\n",
      "Testing Score: 0.9133241815359668\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=W10, final_estimator=W11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF1_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF1_XGB_Results[f\"Test_{str(i)}\"] = RF1_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF1_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a1e33054-2e3b-4d6a-a247-68df8c24bf16",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF1_XGB_Mean\"] = mean(S1_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3235a7d-9fbf-4901-963c-7f8970ad4c8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "be8c7dd5-395f-4d0d-aa69-e7302880f33b",
   "metadata": {},
   "source": [
    "#### KNeighborsRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "23b5c9f0-f88d-4833-84df-c5e5a7803cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF2_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "X10 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "X10.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#W0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "X11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5aaff8bc-a242-4ec0-81e7-7e6ae576b097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8875004286754045\n",
      "Training Score: 0.9762943428250108\n",
      "Testing Score: 0.8875004286754045\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=X0, final_estimator=X1, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF2_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF2_XGB_Results[f\"Test_{str(i)}\"] = RF2_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF2_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c687f996-873d-4b9a-9a43-cd4dfff31f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF2_XGB_Mean\"] = mean(S2_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "007611ad-5f26-41c8-9bf5-587ccaf198a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9cc2276d-02bc-4c51-a5ad-0b3e6a95b29e",
   "metadata": {},
   "source": [
    "#### LassoCV + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bfb4b597-8503-413a-b470-20d9db805224",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF3_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "Y10 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "Y10.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#P0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "Y11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d52e9ea2-abfd-4ba0-80ab-1f65bb7d00f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.48935519671809535\n",
      "Training Score: 0.4929018579495116\n",
      "Testing Score: 0.48935519671809535\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=Y10, final_estimator=Y11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF3_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF3_XGB_Results[f\"Test_{str(i)}\"] = RF3_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF3_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "199064da-84df-4293-be29-31015ead16d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF3_XGB_Mean\"] = mean(RF3_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2988baea-b914-41a4-bd1e-d9d7c0583ca3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1814e575-ddb5-4604-b3d1-97d09703e719",
   "metadata": {},
   "source": [
    "#### DecisionTreeRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "fe452d4f-e55c-44de-af14-4ea6ef66a43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF4_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "Z10 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "Z10.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#P0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model \n",
    "Z11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3e91c733-854b-4c14-88a2-dd7a34e69e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8835479482123538\n",
      "Training Score: 0.9331617340677201\n",
      "Testing Score: 0.8835479482123538\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=Z10, final_estimator=Z11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF4_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF4_XGB_Results[f\"Test_{str(i)}\"] = RF4_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF4_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "108a56d4-0e56-49b8-bdc3-13d500cdfcaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF4_XGB_Mean\"] = mean(RF4_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "539bbac7-04ba-464c-a2e2-1bf08128c7fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a3339084-2597-4229-b59c-e867efdd150d",
   "metadata": {},
   "source": [
    "#### HistGradientBoostingRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "de7b4dce-a089-401e-b7b1-d3635bd4219d",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF5_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AA10 = list()\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "AA10.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AA11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b0b165bd-92ce-4825-9ab9-a92167d66488",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8987422515270717\n",
      "Training Score: 0.9151987491504955\n",
      "Testing Score: 0.8987422515270717\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AA10, final_estimator=AA11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF5_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF5_XGB_Results[f\"Test_{str(i)}\"] = RF5_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF5_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "18be0193-572a-4957-b88f-13494ba4e4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF5_XGB_Mean\"] = mean(RF5_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a360cdcc-a1c8-4fcf-b73d-9c5d576b1a7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b81177b-6d4e-4729-a94b-d8c8c154d173",
   "metadata": {},
   "source": [
    "#### SVR + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "0a2f3797-9d1b-4d73-b888-bfb7213ad6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF6_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AB10 = list()\n",
    "AB10.append(('svr', SVR()))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AB11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cf1e93ff-ea59-47c2-ac00-9e4b3156c59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: -0.009088707768973858\n",
      "Training Score: 0.016370822732102885\n",
      "Testing Score: -0.009088707768973858\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AB10, final_estimator=AB11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF6_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF6_XGB_Results[f\"Test_{str(i)}\"] = RF6_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF6_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d371d1f2-2f64-4ac7-bae9-cfa6ca19b2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF6_XGB_Mean\"] = mean(RF6_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eba4fbcf-556f-4c0b-b666-0db961bdf4ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c58dca26-0605-4064-9238-5e37adb04b04",
   "metadata": {},
   "source": [
    "#### BayesianRidge + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c5cb557d-e5b1-4b5b-99f5-8a825b2d7137",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF7_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AC10 = list()\n",
    "AC10.append(('brr', BayesianRidge(compute_score=True)))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AC11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f121a841-255b-4b5a-874e-8324c8d339d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.6353471544921846\n",
      "Training Score: 0.6743024453326566\n",
      "Testing Score: 0.6353471544921846\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AC10, final_estimator=AC11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF7_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF7_XGB_Results[f\"Test_{str(i)}\"] = RF7_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF7_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "04da19c0-4dc2-4ed7-8a9a-576b8d6bbc14",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF7_XGB_Mean\"] = mean(RF7_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c377b0d-ec7f-4ff9-906c-a5525190baa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e229f85a-fd1c-4250-9161-5761b08c036a",
   "metadata": {},
   "source": [
    "#### SGDRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b54bd660-4806-4c03-9af9-8796ce4a04a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF8_XGB_Results = {}\n",
    "\n",
    "# define the base models\n",
    "AD10 = list()\n",
    "AD10.append(('sgd', make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AD11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "379c2b36-d8a6-45d6-a017-c802f61c33d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.6193198573559692\n",
      "Training Score: 0.6214489160354919\n",
      "Testing Score: 0.6193198573559692\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AD10, final_estimator=AD11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF8_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF8_XGB_Results[f\"Test_{str(i)}\"] = RF8_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF8_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1b2ed5d0-d303-464d-bf8c-f17a19211622",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF8_XGB_Mean\"] = mean(RF8_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f340d2e-b1c8-4af4-b8b6-65a1cd9c7aa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ce7777b6-6045-4485-9a5a-ddd77a1a1996",
   "metadata": {},
   "source": [
    "#### GradientBoostingRegressor + RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "67f03886-ea7e-4757-b09b-4eba3fb4f2ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF9_XGB_Results = {}\n",
    "params = {\n",
    "\"n_estimators\": 46,\n",
    "\"max_depth\": 7,\n",
    "\"learning_rate\": 0.1,\n",
    "\"loss\": \"squared_error\",\n",
    "\"random_state\": 1,}\n",
    "\n",
    "# define the base models\n",
    "AE10 = list()\n",
    "AE10.append(('sgd', ensemble.GradientBoostingRegressor(**params)))\n",
    "#W0.append(('rfr', RandomForestRegressor(n_estimators=128, random_state=0)))\n",
    "#X0.append(('knn', KNeighborsRegressor(n_neighbors=7, algorithm='kd_tree',weights='distance', leaf_size=30,p=1.6)))\n",
    "#Y0.append(('lso', LassoCV(n_alphas=10, random_state=1)))\n",
    "#Z0.append(('dtr', DecisionTreeRegressor(max_depth=15, random_state= 2)))\n",
    "#AA0.append(('hgbr', HistGradientBoostingRegressor(learning_rate=0.1, max_leaf_nodes=80,max_depth=50,min_samples_leaf=19,random_state=1)))\n",
    "# define meta learner model\n",
    "AE11 = RandomForestRegressor(n_estimators=128, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c22a3624-8e57-453a-bf87-1cbfa2dc10ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_0: 0.8969374757757775\n",
      "Training Score: 0.9353686359418488\n",
      "Testing Score: 0.8969374757757775\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    model = StackingRegressor(estimators=AE10, final_estimator=AE11, cv=5)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = model.predict(X_test)\n",
    "    RF9_XGB_R2 = r2_score(y_test, y_pred)\n",
    "    RF9_XGB_Results[f\"Test_{str(i)}\"] = RF9_XGB_R2\n",
    "    print(f\"Test_{str(i)}: {RF9_XGB_R2}\")\n",
    "    print(f\"Training Score: {model.score(X_train, y_train)}\")\n",
    "    print(f\"Testing Score: {model.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4df4b2b3-048a-4cc9-aae2-f88b9512b387",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results[\"RF9_XGB_Mean\"] = mean(RF9_XGB_Results.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "043ee84a-0402-4ff7-badf-3414a09e69c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Results_df = pd.DataFrame.from_dict(Mean_Results, orient='index', columns=['R2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "0e3062a9-57da-4d7e-b450-a2cf70787e02",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S5_XGB_Mean</th>\n",
       "      <td>0.937074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1_XGB_Mean</th>\n",
       "      <td>0.928447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF1_XGB_Mean</th>\n",
       "      <td>0.928447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S9_XGB_Mean</th>\n",
       "      <td>0.926267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4_XGB_Mean</th>\n",
       "      <td>0.904008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF5_XGB_Mean</th>\n",
       "      <td>0.898742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF9_XGB_Mean</th>\n",
       "      <td>0.896937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2_XGB_Mean</th>\n",
       "      <td>0.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF2_XGB_Mean</th>\n",
       "      <td>0.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF4_XGB_Mean</th>\n",
       "      <td>0.883548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S7_XGB_Mean</th>\n",
       "      <td>0.733880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S8_XGB_Mean</th>\n",
       "      <td>0.725923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF7_XGB_Mean</th>\n",
       "      <td>0.635347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF8_XGB_Mean</th>\n",
       "      <td>0.619320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3_XGB_Mean</th>\n",
       "      <td>0.489463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF3_XGB_Mean</th>\n",
       "      <td>0.489355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6_XGB_Mean</th>\n",
       "      <td>0.389269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF6_XGB_Mean</th>\n",
       "      <td>-0.009089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    R2\n",
       "S5_XGB_Mean   0.937074\n",
       "S1_XGB_Mean   0.928447\n",
       "RF1_XGB_Mean  0.928447\n",
       "S9_XGB_Mean   0.926267\n",
       "S4_XGB_Mean   0.904008\n",
       "RF5_XGB_Mean  0.898742\n",
       "RF9_XGB_Mean  0.896937\n",
       "S2_XGB_Mean   0.887500\n",
       "RF2_XGB_Mean  0.887500\n",
       "RF4_XGB_Mean  0.883548\n",
       "S7_XGB_Mean   0.733880\n",
       "S8_XGB_Mean   0.725923\n",
       "RF7_XGB_Mean  0.635347\n",
       "RF8_XGB_Mean  0.619320\n",
       "S3_XGB_Mean   0.489463\n",
       "RF3_XGB_Mean  0.489355\n",
       "S6_XGB_Mean   0.389269\n",
       "RF6_XGB_Mean -0.009089"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean_Results_df.sort_values(by=\"R2\", ascending=False)"
   ]
  }
 ],
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