Simple Linear Regression 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c62c127",
   "metadata": {},
   "source": [
    "## Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.\n",
    "**Salary hike - Build a prediction model for salary hike**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8cf921",
   "metadata": {},
   "source": [
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1da92a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e089c8",
   "metadata": {},
   "source": [
    "### Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0bcd8d2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data = pd.read_csv('D:\\Assignment ExcelR\\Simple Linear Regression\\Salary_Data.csv')\n",
    "salary_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a27597",
   "metadata": {},
   "source": [
    "### Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f37c6bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "077ea0de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "salary_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c57c8517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7bd84939",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
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      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00793bdc",
   "metadata": {},
   "source": [
    "### Plotting the data to check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c70ff0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.boxplot(salary_data['Salary'])\n",
    "plt.title('Salary Hike')\n",
    "plt.subplot(122)\n",
    "plt.boxplot(salary_data['YearsExperience'])\n",
    "plt.title('Years of Experience')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f0056d",
   "metadata": {},
   "source": [
    "#### As we can see there are no Outliers in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abe31b4",
   "metadata": {},
   "source": [
    "### Checking the Correlation between variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "499ae4e9",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9178a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data= salary_data, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39546ed",
   "metadata": {},
   "source": [
    "### Visualization of Correlation beteen x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90336703",
   "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": [
    "sns.regplot(x='YearsExperience', y='Salary', data= salary_data)\n",
    "plt.title('YearsExperience Vs Salary', fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10b9b0c",
   "metadata": {},
   "source": [
    "#### As we can see above\n",
    "* There is good correlation between the two variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bc7e22",
   "metadata": {},
   "source": [
    "### Checking for Homoscedasticity or Hetroscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6fd74516",
   "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": [
    "sns.scatterplot(x ='YearsExperience', y ='Salary', data= salary_data)\n",
    "plt.title('Homoscedasticity', fontweight = 'bold', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3d0394a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    8.053609e+00\n",
       "Salary             7.515510e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca15e18",
   "metadata": {},
   "source": [
    "#### As we can see in above graph\n",
    "* It shows as the Salary Increases the Years of Experience increases variation is constant along the way in data\n",
    "* The data doesn't have any specific pattern in the variation. hence, we can say it's Homoscedasticity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ae453b",
   "metadata": {},
   "source": [
    "### Trying different transformation of data to estimate normal distribution and remove any skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4078074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(salary_data['YearsExperience'], bins = 10, kde = True)\n",
    "plt.title('Before Transformation')\n",
    "sns.displot(np.log(salary_data['YearsExperience']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9fc99c1",
   "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": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(salary_data['YearsExperience'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(salary_data['YearsExperience']), bins = 10, kde = True)\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebfe9ca",
   "metadata": {},
   "source": [
    "#### As we can see\n",
    "* How log transformation affects the data and it scales the values down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ced5c80a",
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EKkxZJ23Z1qRzxgzo2BEmT4Yzzgh9Ao4+OtXh5gxVDYlIhSlzD961a0Nv4BYtYP58eOYZGD9eSaCcqUQgIhUm6R687jB0aBgmesmSME7Q/fdD7dppiLryUyIQkXKRbLPQUnvwfv45dO4cJow55hj4z3/gpJNSGLmoakhEdllRs9Alqzbg/NQsdNj0JcnvZNMm6NkTmjQJTUP79IGpU5UEKkDKEoGZHWRm48xsrpnNNrNbStjGzKyvmS0wsxlm1iJV8YhI6pSpF3BJxo0Ld/933AGXXBJGCO3SBXZXpUVFSOVZ3gJ0dfdpZlYD+MjMRrv7nJhtWgGHR68Tgf7RTxHJIKVV+5SlWeh2vvkmPAx+4QU45JBQHdSqVXmGLklIWYnA3Ze5+7To/VpgLlC8YrA18KwHU4BaZnZAqmISkbJLptqnzBO7FBaGJqCNG8PLL4eSwKxZSgJpUiHPCMysIdAceL/YqvrAVzG/L2bHZIGZdTCzqWY2dfny5SmLU0R2lEy1T5mahU6fHuYHuOGGMG3kzJlw772QlxuzgWWilCcCM6sODAFudfc1xVeX8BHfYYH7AHfPd/f8unXrpiJMEYkjmWqfgub16dWmKfVr5WFA/Vp59GrTdPvWQWvWhOag+flhtNDnn4cxY0KpQNIqpU9izKwqIQm84O5DS9hkMXBQzO8HAktTGZOIlE2yvYHjNgt1D9U/XbrA11+HksB990GtWimKWMoqla2GDHgamOvufeJsNhz4TdR66CRgtbsvS1VMIlJ2uzSf7/z5cOGFcPXVUK8evP8+PP64kkCGSWWJ4FSgHTDTzD6Olt0ONABw9yeBkcBFwAJgPdA+hfGIyE7Yqfl8N26EBx6AXr2gWjV47LEwZHSVKvE/I2lj7jtUyW+/gdmhwGJ332RmZwHNCC19VqU8uhLk5+f71KlT03FoEUnG6NGh+mfBglAS6NMHDlBjwHQzs4/cPb+kdclUDQ0BCs3sMEJVTyPgX+UYn4hUBkuXhgv/+eeDGYwaBS++qCSQBZJJBFvdfQtwOfCIu3cB9JcVkaCwEPr2hSOPhGHD4K9/DcNGn3deuiOTJCXzjGCzmV0D/Ba4NFqmCUFFBD78MMwTMG1aKAk88QQcdli6o5IySiYRtAc6Aj3dfaGZNQKeT21YIlKRkh05dJtVq+D22+HJJ2H//eGll+DKK0OVkGSdUhOBu88xs+781NpnIXB/qgMTkYpRlgnlcQ/jAnXtCitWwM03wz33QM2aFR22lKNSnxGY2aXAx8Bb0e/HmtnwFMclIhUk6ZFD582Dc86Bdu2gYcNQLfTII0oClUAyD4v/CpwArAJw948JLYdEpBIodQiJDRvCoHDNmoVxgvr3h0mTwvSRUikk84xgi7uvtu3r/hJ3PhCRjJLoGUDCISRGjoSbboKFC+Haa+Ghh2C//So6fEmxZEoEs8zs10AVMzvczB4DJqU4LhEpJ6UNI13SEBINN6zkldEPwcUXh57BY8fCc88pCVRSySSCzkATYBPwIrAGuDWFMYlIOSrtGUDsyKG7by3kDzNHMOYfHak3aVyYOvKTT+Dss9MRulSQZFoNrQf+HL1EJMskO4x0wcZF0LFL6Ax20UVhcLhGehyYC+ImAjN7nQTPAtz9spREJCLlqtRhpL/7Dnr0gIED4cADYcgQuPxy9QnIIYlKBA9VWBQikjLdLmi8XT8BiIaRPv8IGDwYunWD778PfQPuugtq1EhfsJIWcROBu79TkYGISGqUNIz0PYfBObf+GiZODNNG9u8fmodKTkpUNfSyu//KzGZS8vSR+lcjkiW2zR62bl3oCXxNn9ARbOBAaN8edquQ6cslQyWqGrol+nlJRQQiIin2+uuhT8CiReHi37s31KmT7qgkA8S9DYiZMvIGd/8y9gXcUDHhicguW7QICgrgsstC/f/EiTBokJKAbJNMebCkQcVblXcgIlLONm8Od/1HHRVmDevdOwwRcdpp6Y5MMkyiZwSdCHf+h5jZjJhVNYD3Uh2YiCSv+BAS99ddxekP3wmzZ4fSwKOPQoMG6Q5TMlSiZwT/At4EegE9YpavdfeVKY1KRJIWO4x07fWrufWNRzh91hjWH3Agew0fDpdeWvpOJKclaj66GlgNXGNmVYD9ou2rm1l1d19UQTGKCPEHjnvw7U/Z+ONmrpoxmh7jB1P9x/X0O+kKhrS6jv9eenG6w5YsUOoQE2Z2E2Eo6m+ArdFiBxI2HzWzQYQWR9+6+y9KWP8zwkxnDaI4HnL3f5YleJFckWjymJqfzaHv209w3NJ5vH/QL7jjvE7Mr3swtj6dEUs2SWYY6luBxu7+XRn3PRh4HHg2zvobgTnufqmZ1QU+NbMX3P3HMh5HpNIraeC43X5Yy4abb+X1SUNZXa06f7i4C0ObtNw2NMS2ISRESpFMIviKUEVUJu4+wcwaJtoEqGFhooPqwEpgS1mPI5ILths4zp0LP5vEXWMGcMAP37GwTVuuadSar3ffa9smeVWr0O2CxmmIVLJRMongc2C8mb1BGIoaAHfvs4vHfhwYDiwltES6yt23lrShmXUAOgA0UMsHyUFFA8cdtOpr7h79JC0/n8qcnzfirnZ/ZUC/m+hR1snnRWIkkwgWRa89old5uYAwF3JL4FBgtJlNdPc1xTd09wHAAID8/HzNjiY5p/vZDfm8x910fPffbNmtCve0vJ6XT2rNfVccC8QMISGyE5KZj+DuFB27PXC/uzuwwMwWAkcCH6ToeCLZadw4LuvUCT79lLG/OIPbz/g/qhx0EPfprl/KSTKthuoCfyLMUrZn0XJ3b7mLx14EnANMNLP9gMaEaigRAfjmmzA09AsvwCGHwMiRtGzViinpjksqnWSGmHgBmAc0Au4GvgA+LO1DZvYiMBlobGaLzex3ZtbRzDpGm9wLnBKNbvpfoLu7r9iJ7yBSuRQWhmGhGzeGl1+GO+6AWbOglUZ2kdRI5hnBvu7+tJndEs1R8I6ZlTpXgbtfU8r6pcD5ScYpUukNm76E1wa9zq1D+nDMsvksP/5U6j73dEgIIimUTCLYHP1cZmYXE1r5HJi6kERyz4iJ81jTtQcDp77Oyr1qcvOlf2R0s5b0Wl+dgnQHJ5VeMongvqgXcFfgMaAm0CWlUYnkCnd4+WVOuv5Gaq9dyXMtLuLvp7djzZ7VYctWHnz7Uz0QlpRLptXQiOjtauDs1IYjkkPmzw8TxYwaxdL9D+P/Lv8zMw44YrtNlpYw6bxIeUum1dA/KXmqyv9LSUQildiw6Ut4dMRMLnv7OW54/xV223NPqj72GDeuacxXa3YcXUXDREhFSKZqaETM+z2BywnPCUSkDIZNX8LrDz3DoDcfp9H3yxh+1Bk8dP7/4w+nnklX2G5QOdAwEVJxkqkaGhL7e9QsdEzKIhKpjJYupfp11/L0jPF8vk89rv3VvbzbqDkQBpR7r0fLbe81TIRUtGRKBMUdThg6WkRKEDtvwIE19+CJVZNp9uRDnL5+I31Oa8tTJ/6STbv/NFpL0XMADRMh6ZLMM4K1hGcEFv38Guie4rhEslLsvAHNln1Gz8FP0PSb//HNSWfS+dTf8cHutXf4jJ4DSLolUzVUoyICEakMHnz7U6quXc3tE56l7fQ3WV59H268rDsfn3Qe3S48kpl6DiAZKGEiMLM8oC1wdLRoKvAfTR4jUgJ3jn9vJH8e+zS1N6zhmeMu4e+nt+OHanthqzduq/bRcwDJNHETgZk1BV4H3gE+IlQNXQB0MbPzgD+6+x0VEqVIpps3D268kUfGjuXjA47guiv/yuz9D9u2uqj6R88BJBMlKhH0Ba5399GxC83sXGAWMDuVgYlkhQ0b4G9/gwcegL335uPbe9GWpqyLmVVS1T+S6RKNPnpA8SQA4O5jCOMPXZ6yqESywciR0KQJ3HcfXHUVzJvHsT170POKY6lfKw8D6tfKo1ebpioFSEZLVCLYzcyqufum2IVmtiew2d3XpzY0kQy1eDFLrvt/1P/vSBbUPpBHf/8Q59xwNQX77Qeo+keyT6JE8CwwxMxucvcvAKLJ6PsCz6U+NJEMs2UL9O3L5r/cyb4/bqb3Gb/hHydczuYqVRkzdCaAEoBkpbiJwN3vM7ObgAlmtle0eB3wkLs/ViHRiWSKyZOhY0eYMYMPG59I97Ou56ta+29bvWFzoUYKlayVsPmouz8OPG5mNaLf11ZIVCKZYuVK6NED/vEPOPBAGDKEtu/vgZvtsKlGCpVslcxUlbj7WiUBySnuMHhwmB1s0KAwd/DcudCmDfX22avEj6iHsGSrpBKBSE6ZPRvOPBPat4cjjoBp0+Chh6B6dQC6XdCYvKpVtvuImohKNlMiECmybh107w7HHhuSwcCBMHEiNGu23WYFzevTq01TNRGVSiOZQef2IkxT2cDdrzezw4HGMTOXiWS/4cOhc2dYtCiUBHr3hjp14m6uJqJSmSRTIvgnsAk4Ofp9MXBfaR8ys0Fm9q2ZzUqwzVlm9rGZzTazd5KKWKQ8LVoEBQXQujXUqBFKAIMGQZ06DJu+hFPvH0ujHm9w6v1jGTZ9SbqjFUmJZBLBoe7em9CbGHffQBh3qDSDgQvjrTSzWkA/4DJ3bwJcmcQ+RcrH5s3hrv+oo2D06DBExPTpcNppwE/DSS9ZtQEHlqzawG1DZyoZSKWUzMQ0P0ajkDqAmR1KKCEk5O4Tog5o8fwaGOrui6Ltv00iFpEyi50opl6tPO6vu4rTH74TZs9m2VkXcOOJv2X6yprU+/vEbaOBPvj2p9sNFw3qKyCVVzKJ4C7gLeAgM3sBOBW4rhyOfQRQ1czGAzWAR9392ZI2NLMOQAeABg00OZokL3aimNrrV3PrG49w+qwxrD/gQGY8/E/af7f/tgt+0V0/xO8ToL4CUhklMzHNaDObBpxEqBK6xd1XlNOxjwPOAfKAyWY2xd0/KyGGAcAAgPz8fC+HY0slUvyOP3aM/wff/pSNP27mqhmj6TF+MNV/XE//E6/gPxddx8aNeWzYvP2Fveiuv16tPJaUcNFXXwGpjBLNR9Ci2KJl0c8GZtbA3aft4rEXAyvcfR2wzswmAMcAOyQCkXhi7/hh+7v6gub1qfnZHPq+/QTHLZ3H+wf9gjvO68T8ugdj64H18e/6H77q2O32C+orIJVXohLB3xOsc6DlLh77NcLwFbsDewAnAg/v4j4lx8Sry39i+HQKnu/D6888wupq1el6UReG/KIlRENDFN3Zx7vr12xikksSDTp39q7s2MxeBM4C6pjZYsKzhqrRvp9097lm9hYwA9gKDHT3uE1NRUqyQ529Oxd+Nom7xgyAH77jqzZtuaZRa77e/adhIWLv7BPd9auvgOSKZDqU7QncAJxGKAlMBJ50942JPufu15S2b3d/EHgwuVAll8V7DhBbl3/Qqq+5e/STtPx8KvP3PwRGvU6jk0+mR4JnCKC7fhFzT/zs1cxeBtYCz0eLrgH2cfe0tPvPz8/3qVOnpuPQkibFnwNAuHPv1aYpAHe+/BHt3vsPnSe/xJbdqvD4me04qufttD7+4HSFLJJxzOwjd88vaV0yzUcbu/sxMb+PM7NPyic0kdIlatP/3onGOf/+AzW++B9vHnEKT7W5met+dRqtdVcvkrRkEsF0MzvJ3acAmNmJwHupDUvkJyW13a+z7nv+OOLvcNs4ajRqBCNH0qpVK1qlIT6RbJdMIjgR+I2ZLYp+bwDMNbOZgLt7s/gfFdl1sc8BdttayK8/eZtu7zzDXls2wR13wO23Q57a94vsrGQSQdzxgkQqQrcLGnPb0Jkc8tWn9BzVj2OXfcaUg4/hhz6Pcm6bM9MdnkjWS6Zn8Zdmtg9wUOz25dChTCQpBYfWoOn/XqHhvwfzfV5N7v7VbRzT/QYKWhyY7tBEKoVkmo/eSxhb6H9EA89RPh3KRBJzh5dfhi5dOPTrr6FTJ+r07MldtWqlOzKRSiWZqqFfEYai/jHVwYhsM38+3HQTjBoFLVrAa6/B8cenOyqRSimZ+QhmAbVSHIdIsHEj3H03NG0KU6ZA377wwQdKAiIplEyJoBehCeksYuYhcPfLUhaV5KbRo+GGG2DBArj6aujTBw44IN1RiVR6ySSCZ4AHgJmEMYFEyiTRMNEAb436iN27/ZFzZ4xn0b71+arfvzi1U6kjlIhIOUkmEaxw974pj0QqpYTDRDfbnxm39eS0R++nauEW+pzWlqdO/CW7Lc2j1/QlGvNHpIIkkwg+MrNewHC2rxpS81EpVbzhIV4f+BoFU56m2bRpTGjYnL+c34kv96kXNtCUkCIVKplE0Dz6eVLMMjUflaQUHx6i5sYf6DbhWdpOfxMO2J+bLuvOiCNP2zZPQLzPiUjqJNOhbJfmJZDKrbT6/23DQ7hTMGc8fx77NLU3rOE/p1zOr978J9P7TQVNCSmSVsk0H8XMLjazP5nZnUWvVAcmma+o/n/Jqg04P9X/D5u+ZNs23S5ozNGrl/Kvf/+ZR0b8nSU/+zlX/u5R9ni8L9SsSbcLGpNXtcp2+9WUkCIVK5mexU8CewFnAwOBK4APUhyXZIFEw0MXNK8PGzZQMKQ/lw3szbrd9+CO82/gnTML6Nrq6G2lBk0JKZJ+yUxMM8Pdm8X8rA4MdffzKybE7WlimopRWpUPQKMeb1DSvx4DFp5hoWfwwoVw7bXw0EOw334VEruI7CjRxDTJVA0VVeCuN7N6wGagUXkFJ5knmSofKLkef/81Kxj0xgNw8cVQrRqMHQvPPackIJLBkkkEI8ysFmFu4WnAF8CLKYxJ0ixRlU+s2Pr9KlsL+d0Hr/LfpztxxvwPoGdP+OQTOFttDUQyXTKthu6N3g4xsxHAnu6+OrVhSTrFa7pZfHlRVdHIAUPpMqQPRy3/gq9Pa8nezw6ERio0imSLuCUCMzvezPaP+f03wMvAvWZWu7Qdm9kgM/s2GqMo0XbHm1mhmV1RlsAldeI13dxh+cqVFPS/mwFP3sxR1bbAkCHsP2GMkoBIlklUNfQU8COAmZ0B3A88C6wGBiSx78GUMruZmVUhjGP0dhL7kwpSapNOdxg8GBo3hkGDoGtXmDsX2rTZoWOYiGS+RFVDVdx9ZfT+KmCAuw8hVBF9XNqO3X2CmTUsZbPOwBBAYwxnkIRNOmfPhk6dYOJEOOUU6N8fmmnaapFsljARmNnu7r4FOAfokOTnkmJm9YHLCUNVJEwEZtah6PgNGjTY1UNLEgqa19++uei6ddC9exgaumZNGDgQ2reH3ZLqkygiGSzRBf1F4B0zW0FoQjoRwMwOI1QP7apHgO7uXmilVCe4+wCi6qj8/PzEHR+k/A0fDp07w6JF4eLfuzfUqZPuqESknMRNBO7e08z+CxwAjPKfep7tRqjS2VX5wL+jJFAHuMjMtrj7sHLYt5SHL7+Em28OiaBJk1AddNpp6Y5KRMpZwioed59SwrLPyuPA7r6taYmZDQZGKAlkiM2b4eGHw5SREEoAt94KVaumNSwRSY1druuPx8xeBM4C6pjZYuAuoCqAuz+ZquPKLpowITwMnjMHCgrg0UdBz2VEKrWUJQJ3T3quQXe/LlVxSJKWL4c//Sk0Cz344FAddOml6Y5KRCqAmnzkuq1bQwugxo3h+eehR4/QRFRJQCRnpKxEIFlgxgzo2BEmT4Yzzgh9Ao4+Ot1RiUgFU4kgF61dG3oDt2gB8+eH6qDx45UERHKUSgS5xB2GDoVbboElS6BDB+jVC2qXOnSUiFRiKhHkis8/h0sugSuuCJ3BJk2Cp55SEhARJYJKb9OmMDdAkyahaWifPjB1Kpx8crojE5EMoURQmY0bB8ccA3fcAZdcwluvjOXUTcfQ6I63OfX+sTvMOCYiuUmJoDL65hto1w5atgy9hEeOZNjtj9Dlve9KnX5SRHKPEkFlUlgYmoAeeSS89FIoCcyaBa1aJT39pIjkHrUaqiymTQtDQ3zwQSgJ9OsXOolFkp1+UkRyj0oE2W7NmtAc9Pjjw2ihzz8PY8ZslwSgDNNPikjOUSLIVu6h+ufII+Gxx0IP4XnzoG3bEqeLLHX6SRHJWUoE2Wj+fLjwQrj6auZZdVq3+zunHvRLhi1cF/cjBc3r06tNU+rXysOA+rXy6NWm6fazkIlITtIzgmyycSM88AD06sXmqnvwwAUdGdSsFVt3qwJRKyAg7sV9h+knRURQIsgeo0fDDTfAggVw9dX8slEbZmzda7tNiloB6WIvImWhqqFMt2wZXHMNnH9+qPsfNQpefJGZxZJAEbUCEpGyUiLIVIWF0LdvaP3z6qth2sgZM+C88wC1AhKR8qNEkIk+/BBOOCE0Cz355NAp7M47Yc89t22iVkAiUl6UCDLJqlVw441w4omhSuill+Ctt+Cww3bYVK2ARKS86GFxJnCHf/0rTBazfDncfDPccw/UrJnwY2oFJCLlIWUlAjMbZGbfmtmsOOvbmtmM6DXJzI5JVSwZbd48OPdcuPbaMGn81KnwyCOlJgERkfKSyqqhwcCFCdYvBM5092bAvcCAFMaSeTZsCIPCNWsWxgnq3z9MFtO8ebojE5Eck7KqIXefYGYNE6yfFPPrFODAVMWScUaOhJtugoULQ0ngoYdgv/3SHZWI5KhMeVj8O+DNdAeRcosXh6kiL74YqlWDsWPhueeUBEQkrdKeCMzsbEIi6J5gmw5mNtXMpi5fvrzigisvW7aEKSKPOgreeCNMHfnJJ3D22emOTEQkvYnAzJoBA4HW7v5dvO3cfYC757t7ft26dSsuwPIweTIcd1xoEXTGGTB7Ntx+O+yxR7ojExEB0pgIzKwBMBRo5+6fpSuOlFm5Ejp0gFNOCe+HDIERI+CQQ9IdmYjIdlL2sNjMXgTOAuqY2WLgLqAqgLs/CdwJ7Av0szB+/hZ3z09VPBXGHZ55Brp1g++/DyWBv/4VqldPd2QiIiVKZauha0pZ/3vg96k6flrMnh2mi5w4MZQE+vcPzUNFRDKYehaXh3XrQk/gPn1CR7CBA6F9e9it9Jq3YdOX8ODbn7J01Qbq1cqj2wWN1VtYRCqUEsGuGj4cOneGRYvCxb93b6hTJ6mPDpu+hNuGzmTD5kIAliQxuYyISHlLe/PRrPXll9C6dXjVqBGqgwYNSjoJADz49qfbkkCRosllREQqihJBWW3eHKaLPPpoGDMmlACmT4fTTivzruJNIqPJZUSkIqlqqCwmTAgPg+fMgYICePRRaNBgp3dXr1YeS0q46GtyGRGpSCoRJGP58lD/f+aZ4cHw8OFh1rBdSAKgyWVEJDOoRJDI1q3w9NPQvTusXQs9eoQRQ/feu1x2X/RAWK2GRCSdlAji+eSTUA00eXIYGqJ///BcoJxpchkRSTclguLWroW77goTx++zT+gl3K4dhN7PgNr+i0jlokRQxB2GDg0Txi9ZEsYJ6tULatfebjO1/ReRykYPiwE+/zzMEXDFFaEfwOTJ8NRTOyQBUNt/Eal8cjsRbNoU5gZo0iR0COvTJ8wZfNJJcT+itv8iUtnkbtXQuHHhYfCnn4aSwCOPQP3Sq3bU9l9EKpvcKxF8802YJ7hly9BL+M034ZVXkkoCoLb/IlL55E6JoLAQBgyA226D9etDf4Dbb4e8st3Jq+2/iFQ2uZEIpk2Djh3hww9DSaBfP2i883fwavsvIpVJ5a4aWr0abr4Zjj8+DBP9wgthoLhdSAIiIpVNpSgR7NDB6/wjKJj/HnTpAl9/DTfcAPfdB7VqpTtUEZGMk/WJoHgHr6qfL6DuFX+EhdOhRQt47bVQIhARkRJlfSIo6uBVbcuPdJryCp2m/IdNVary8KU30eXVR6BKlVL3ISKSy7I+ESxdtYFGK5cw6D9/pdH3yxh+1Bnc2/L3rKhemy5KAiIipUpZIjCzQcAlwLfu/osS1hvwKHARsB64zt2nlfU49WrlsXRLXb7Ypx5/Oe8G3m3UHID66uAlIpKUVLYaGgxcmGB9K+Dw6NUB6L8zB+l2QWN2y8uj/ZV3b0sC6uAlIpK8lCUCd58ArEywSWvgWQ+mALXM7ICyHqegeX16tWlK/Vp5GKEk0KtNU7XzFxFJUjqfEdQHvor5fXG0bFnxDc2sA6HUQIMSpodUBy8RkZ2Xzg5lVsIyL2lDdx/g7vnunl+3bt0UhyUiklvSmQgWAwfF/H4gsDRNsYiI5Kx0JoLhwG8sOAlY7e47VAuJiEhqpbL56IvAWUAdM1sM3AVUBXD3J4GRhKajCwjNR9unKhYREYkvZYnA3a8pZb0DN6bq+CIikpzKPfqoiIiUysKNefYws+XAl+mOo5g6wIp0B1EG2RRvNsUK2RVvNsUK2RVvJsZ6sLuX2Owy6xJBJjKzqe6en+44kpVN8WZTrJBd8WZTrJBd8WZTrKCqIRGRnKdEICKS45QIyseAdAdQRtkUbzbFCtkVbzbFCtkVbzbFqmcEIiK5TiUCEZEcp0QgIpLjlAh2gpldaWazzWyrmcVtImZmX5jZTDP72MymVmSMMTEkG+uFZvapmS0wsx4VGWOxOGqb2Wgzmx/93CfOdmk7t6Wdq2j8rL7R+hlm1qIi4yshntLiPcvMVkfn8mMzuzMdcUaxDDKzb81sVpz1GXNuk4g1Y85rqdxdrzK+gKOAxsB4ID/Bdl8AdTI9VqAK8D/gEGAP4BPg6DTF2xvoEb3vATyQSec2mXNFGEPrTcJQ6ycB76fx759MvGcBI9IVY7FYzgBaALPirM+kc1tarBlzXkt7qUSwE9x9rrt/mu44kpFkrCcAC9z9c3f/Efg3YQa5dGgNPBO9fwYoSFMc8SRzrspl9r1ykkl/21J5Bc1sWB6SiDVrKBGklgOjzOyjaJa1TBVvtrh02M+j4cijnz+Ps126zm0y5yqTzmeysZxsZp+Y2Ztm1qRiQtspmXRuk5EV5zWdU1VmNDMbA+xfwqo/u/trSe7mVHdfamY/B0ab2bzoLqJclUOsSc8WVx4SxVuG3VTIuS1BMueqQs9nKZKJZRphHJofzOwiYBhweKoD20mZdG5LkzXnVYkgDnc/txz2sTT6+a2ZvUooppf7xaocYq3Q2eISxWtm35jZAe6+LCryfxtnHxVybkuQzLnKpNn3So3F3dfEvB9pZv3MrI67Z9qgaZBZ5zahbDqvqhpKETPb28xqFL0HzgdKbF2QAT4EDjezRma2B3A1YQa5dBgO/DZ6/1tghxJNms9tMucqk2bfKzVeM9vfzCx6fwLhuvBdhUeanEw6twll1XlN99PqbHwBlxPuTDYB3wBvR8vrASOj94cQWmh8AswmVNNkZKzR7xcBnxFamKQl1iiOfYH/AvOjn7Uz7dyWdK6AjkDH6L0BT0TrZ5KgZVmGxHtTdB4/AaYAp6Qx1heBZcDm6N/t7zL13CYRa8ac19JeGmJCRCTHqWpIRCTHKRGIiOQ4JQIRkRynRCAikuOUCEREcpwSgaSFme0bMyrj12a2JHq/yszmVHAsBWZ2dMzv95hZmTvpmVnDBCNRNjGzsWb2mZn9z8zuNrNy//+X6LuY2XhLMAKt5C4lAkkLd//O3Y9192OBJ4GHo/fHAlvL+3hmlqgXfQGw7eLp7ne6+5hyPHYeoSPU/e5+BNCU0BP6lvI6RowCUvhdpHJSIpBMVMXM/mFhHoVR0YUUMzvUzN6KBpqbaGZHRssPNrP/RuPT/9fMGkTLB5tZHzMbBzxQ0ufN7BTgMuDBqERyaPS5K6J9HG9mk6KBwz4wsxrRnf9EM5sWvU4p5fv8GnjP3UcBuPt6QmejbtEx/mpmfyza2MxmmVnD6P2wKN7ZFjO4npn9YGY9o7immNl+pX2XWGZ2vplNjuJ/xcyqR8vvN7M50bl8qOx/OslGSgSSiQ4HnnD3JsAq4JfR8gFAZ3c/Dvgj0C9a/jhhaOJmwAtA35h9HQGc6+5dS/q8u08i3K13i0oo/yv6YDQkw0vALe5+DHAusIEw/tF57t4CuKrY8UrSBPgodkF0nDwzq1XKZ/8vijcfuNnM9o2W7w1MieKaAFyf6LvEMrM6wB3ReWkBTAX+YGa1CT3Rm0Tn8r5SYpNKQoPOSSZa6O4fR+8/AhpGd6ynAK9Ew7cAVIt+ngy0id4/R5jcpsgr7l5YyufjaQwsc/cP4adBxCyMb/S4mR0LFBKSTSJGySNkljSSZnE3m9nl0fuDCEnyO+BHYES0/CPgvCT2VeQkQvXRe9G52AOYDKwBNgIDzeyNmP1LJadEIJloU8z7QiCPUHpdFT1HKE3sRXdd9LMsny8S7wLehTBu0zHRfjeWsp/ZhNmsftqx2SHACndfZWZb2L50vme0zVmEUsjJ7r7ezMYXrQM2+0/jwxRStv/LBox292t2WBEGRzuHMDjdTUDLMuxXspSqhiQrRHfjC83sStg2d+0x0epJhAsXQFvg3TJ+fi1Qo4TDzgPqmdnx0WdqRA+df0YoKWwF2hGmg0zkBeC0mNY7eYTqpLui9V8QpjzEwhy8jaLlPwO+j5LAkYQ7+dLE+y6xpgCnmtlh0TH3MrMjolLTz9x9JHAr4cG95AAlAskmbYHfmVnRqKNFUy7eDLQ3sxmEC3O81jjxPv9voJuZTTezQ4s29jC141XAY9FnRhPuyPsBvzWzKYRqoXUk4O4bCA9x/2xmnwErCA+PX4g2GQLUNrOPgU6EkUIB3gJ2j77XvYQLeGlK/C7F4lkOXAe8GO17CnAkIYGMiJa9Qyj5SA7Q6KMiFczMCoA+wNnu/mWawxFRIhARyXWqGhIRyXFKBCIiOU6JQEQkxykRiIjkOCUCEZEcp0QgIpLj/j/GTubN8KTkYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(np.log(salary_data['YearsExperience']), line = 'r')\n",
    "plt.title('No transformation')\n",
    "sm.qqplot(np.sqrt(salary_data['YearsExperience']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "sm.qqplot(np.sqrt(salary_data['YearsExperience']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "sm.qqplot(np.cbrt(salary_data['YearsExperience']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a023a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(salary_data['Salary'], bins = 10, kde = True)\n",
    "plt.title('Before Transformation')\n",
    "sns.displot(np.log(salary_data['Salary']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "845a2483",
   "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": [
    "labels = ['Before Transformation','After Transformation']\n",
    "sns.distplot(salary_data['Salary'], bins = 10, kde = True)\n",
    "sns.distplot(np.log(salary_data['Salary']), bins = 10, kde = True)\n",
    "plt.title('After Transformation')\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f36cac41",
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "sm.qqplot(salary_data['Salary'], line = 'r')\n",
    "plt.title('No transformation')\n",
    "sm.qqplot(np.log(salary_data['Salary']), line = 'r')\n",
    "plt.title('Log transformation')\n",
    "sm.qqplot(np.sqrt(salary_data['Salary']), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "sm.qqplot(np.cbrt(salary_data['Salary']), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a555d1",
   "metadata": {},
   "source": [
    "#### Important Note:\n",
    "* We only Perform any data transformation when the data is skewed or not normal distribution N(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a2c515",
   "metadata": {},
   "source": [
    "### Fitting a Linear Regression Model\n",
    "### Using Ordinary Least Squares (OLS) regression\n",
    "**It is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable; the method estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the dependent variable configured as a straight line**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "925afe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = smf.ols('Salary~YearsExperience', data = salary_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0afd49c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared     : 0.957\n",
      "Adj.R-squared : 0.955\n",
      "AIC Value     : 606.882\n",
      "BIC Value     : 609.685\n"
     ]
    }
   ],
   "source": [
    "print('R-squared     :',linear_model.rsquared.round(3)) #Overall Contribution of Predictors\n",
    "print('Adj.R-squared :',linear_model.rsquared_adj.round(3)) #Overall Contribution of Predictors\n",
    "print('AIC Value     :',linear_model.aic.round(3)) #Error Impurity\n",
    "print('BIC Value     :',linear_model.bic.round(3)) #Error Impurity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05935c8a",
   "metadata": {},
   "source": [
    "#### As we can notice in the above model\n",
    "* The R-squared and Adjusted R-squared scores are above 0.85. (It is a thumb rule to consider Adjusted R-squared to be greater than 0.85 for a good model for prediction)\n",
    "* AIC and BIC score are much higher for this model\n",
    "* Lets Try some data transformation to check whether these scores can get any better than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23efc1fa",
   "metadata": {},
   "source": [
    "### Square Root transformation on data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec1ea7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model1 = smf.ols('np.sqrt(Salary)~np.sqrt(YearsExperience)', data = salary_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4eb5c5d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared     : 0.942\n",
      "Adj.R-squared : 0.94\n",
      "AIC Value     : 237.046\n",
      "BIC Value     : 239.848\n"
     ]
    }
   ],
   "source": [
    "print('R-squared     :',linear_model1.rsquared.round(3))\n",
    "print('Adj.R-squared :',linear_model1.rsquared_adj.round(3))\n",
    "print('AIC Value     :',linear_model1.aic.round(3))\n",
    "print('BIC Value     :',linear_model1.bic.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999f81f8",
   "metadata": {},
   "source": [
    "#### As we can notice in the above model\n",
    "* The R-squared and Adjusted R-squared scores are above 0.85. but its has gotten less than previous model\n",
    "* AIC and BIC score are now much better for this model\n",
    "* Lets Try some data transformation to check whether these scores can get any better than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773e49d1",
   "metadata": {},
   "source": [
    "### Cube Root transformation on data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "350e42a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model2 = smf.ols('np.cbrt(Salary)~np.cbrt(YearsExperience)', data = salary_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8269dcca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared     : 0.932\n",
      "Adj.R-squared : 0.93\n",
      "AIC Value     : 105.179\n",
      "BIC Value     : 107.981\n"
     ]
    }
   ],
   "source": [
    "print('R-squared     :',linear_model2.rsquared.round(3))\n",
    "print('Adj.R-squared :',linear_model2.rsquared_adj.round(3))\n",
    "print('AIC Value     :',linear_model2.aic.round(3))\n",
    "print('BIC Value     :',linear_model2.bic.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546c4cd6",
   "metadata": {},
   "source": [
    "#### As we can notice in the above model\n",
    "* After Cube root transformation on the Data, R-squared and Adjusted R-squared scores have decreased but they are above 0.85.\n",
    "* Lets Try other data transformation to check whether these scores can get any higher than this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d7cbf9",
   "metadata": {},
   "source": [
    "### Log transformation on data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7307858b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "linear_model3 = smf.ols('np.log(Salary)~np.log(YearsExperience)', data = salary_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "330c9afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared     : 0.905\n",
      "Adj.R-squared : 0.902\n",
      "AIC Value     : -42.417\n",
      "BIC Value     : -39.615\n"
     ]
    }
   ],
   "source": [
    "print('R-squared     :',linear_model3.rsquared.round(3))\n",
    "print('Adj.R-squared :',linear_model3.rsquared_adj.round(3))\n",
    "print('AIC Value     :',linear_model3.aic.round(3))\n",
    "print('BIC Value     :',linear_model3.bic.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c0ff2c",
   "metadata": {},
   "source": [
    "#### As we can notice in the above model\n",
    "* After log transformation on the data, This Model scored has not good R-squared and Adjusted R-squared scores than the previous model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1318bc9",
   "metadata": {},
   "source": [
    "### Model Testing\n",
    "**As Y = Beta0 + Beta1*(X)**\n",
    "### Finding Coefficient Parameters (Beta0 and Beta1 values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "651a241b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2afb2acb",
   "metadata": {},
   "source": [
    "**Here, (Intercept) Beta0 value = 25792.20 & (YearsExperience) Beta1 value = 9449.96**\n",
    "\n",
    "**Hypothesis testing of X variable by finding test_statistics and P_values for Beta1 i.e if (P_value < α=0.05 ; Reject Null)**\n",
    "\n",
    "**Null Hypothesis as Beta1=0 (No Slope) and Alternate Hypthesis as Beta1≠0 (Some or significant Slope)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "654afad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept          11.346940\n",
      "YearsExperience    24.950094\n",
      "dtype: float64 \n",
      " Intercept          5.511950e-12\n",
      "YearsExperience    1.143068e-20\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(linear_model.tvalues,'\\n',linear_model.pvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6e9d49",
   "metadata": {},
   "source": [
    "**(Intercept) Beta0: tvalue=11.34 , pvalue=5.511950e-12**\n",
    "\n",
    "**(daily) Beta1: tvalue=24.95, pvalue= 1.143068e-20**\n",
    "\n",
    "**As (pvalue=0)<(α=0.05); Reject Null hyp. Thus, X(YearsExperience) variable has good slope and variance w.r.t Y(Salary) variable.**\n",
    "\n",
    "**R-squared measures the strength of the relationship between your model and the dependent variable on a 0 – 100% scale.**\n",
    "\n",
    "**Measure goodness-of-fit by finding rsquared values (percentage of variance)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bbf1864a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.rsquared, linear_model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b952fb6",
   "metadata": {},
   "source": [
    "**Determination Coefficient = rsquared value = 0.95 ; very good fit >= 85%**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b629b567",
   "metadata": {},
   "source": [
    "### Residual Analysis\n",
    "### Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "22cd061d",
   "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": [
    "sm.qqplot(linear_model.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model without any data transformation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd90ff",
   "metadata": {},
   "source": [
    "#### As we can notice in the above plot\n",
    "* The first model follows normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5d7b2a",
   "metadata": {},
   "source": [
    "### Residual Plot to check Homoscedasticity or Hetroscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55d4d33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85474a06",
   "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": [
    "plt.scatter(get_standardized_values(linear_model.fittedvalues), get_standardized_values(linear_model.resid))\n",
    "plt.title('Residual Plot for Model without any data transformation')\n",
    "plt.xlabel('Standardized Fitted Values')\n",
    "plt.ylabel('Standardized Residual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d70bb1",
   "metadata": {},
   "source": [
    "#### As we can notice in the above plots\n",
    "* The Model have Homoscedasciticity.\n",
    "* The Residual(i.e Residual = Actual Value - Predicted Value) and the Fitted values do not share any Pattern.\n",
    "* Hence, there is no relation between the Residual and the Fitted Value. It is Randomly distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688492d9",
   "metadata": {},
   "source": [
    "### Model Validation\n",
    "**Comparing different models with respect to the Root Mean Squared Errors**\n",
    "\n",
    "**We will analyze Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) — AKA the average distance (squared to get rid of negative numbers) between the model’s predicted target value and the actual target value.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a8c9a733",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fef15bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model1_pred_y =np.square(linear_model1.predict(salary_data['YearsExperience']))\n",
    "linear_model2_pred_y =pow(linear_model2.predict(salary_data['YearsExperience']),3)\n",
    "linear_model3_pred_y =np.exp(linear_model3.predict(salary_data['YearsExperience']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f66157a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Model = 5788.315051119395 \n",
      "Linear Model1= 5960.647096174306 \n",
      "Linear Model2= 6232.815455835857 \n",
      "Linear Model3= 7219.716974372746\n"
     ]
    }
   ],
   "source": [
    "linear_model1_rmse =np.sqrt(mean_squared_error(salary_data['Salary'], linear_model1_pred_y))\n",
    "linear_model2_rmse =np.sqrt(mean_squared_error(salary_data['Salary'], linear_model2_pred_y))\n",
    "linear_model3_rmse =np.sqrt(mean_squared_error(salary_data['Salary'], linear_model3_pred_y))\n",
    "print('Linear Model =', np.sqrt(linear_model.mse_resid),'\\n' 'Linear Model1=', linear_model1_rmse,'\\n' 'Linear Model2=', linear_model2_rmse,'\\n' 'Linear Model3=', linear_model3_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7fb02f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Linear Model'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = {'Linear Model': np.sqrt(linear_model.mse_resid), 'Linear Model1': linear_model1_rmse, 'Linear Model2': linear_model3_rmse, 'Linear Model3' : linear_model3_rmse}\n",
    "min(rmse, key=rmse.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db89e8e",
   "metadata": {},
   "source": [
    "**As 'Linear Model' has the minimum RMSE and highest Adjusted R-squared score. Hence, we are going to use 'Linear Model' to predict our values**\n",
    "\n",
    "**'Linear Model' is that Simple Linear Regression model where we did not perfrom any data transformation and got the highest Adjusted R-squared value**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e715fd",
   "metadata": {},
   "source": [
    "### Predicting values\n",
    "### First model results without any transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e70f3a40",
   "metadata": {
    "scrolled": false
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Predicted_Salary_Hike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "      <td>36187.158752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "      <td>38077.151217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "      <td>39967.143681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "      <td>44692.124842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "      <td>46582.117306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "      <td>53197.090931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "      <td>54142.087163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "      <td>56032.079627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "      <td>56032.079627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "      <td>60757.060788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "      <td>62647.053252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "      <td>63592.049484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "      <td>63592.049484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "      <td>64537.045717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "      <td>68317.030645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "      <td>72097.015574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "      <td>73987.008038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "      <td>75877.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "      <td>81546.977895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "      <td>82491.974127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "      <td>90051.943985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "      <td>92886.932681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "      <td>100446.902538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "      <td>103281.891235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "      <td>108006.872395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "      <td>110841.861092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "      <td>115566.842252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "      <td>116511.838485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "      <td>123126.812110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "      <td>125016.804574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary  Predicted_Salary_Hike\n",
       "0               1.1   39343.0           36187.158752\n",
       "1               1.3   46205.0           38077.151217\n",
       "2               1.5   37731.0           39967.143681\n",
       "3               2.0   43525.0           44692.124842\n",
       "4               2.2   39891.0           46582.117306\n",
       "5               2.9   56642.0           53197.090931\n",
       "6               3.0   60150.0           54142.087163\n",
       "7               3.2   54445.0           56032.079627\n",
       "8               3.2   64445.0           56032.079627\n",
       "9               3.7   57189.0           60757.060788\n",
       "10              3.9   63218.0           62647.053252\n",
       "11              4.0   55794.0           63592.049484\n",
       "12              4.0   56957.0           63592.049484\n",
       "13              4.1   57081.0           64537.045717\n",
       "14              4.5   61111.0           68317.030645\n",
       "15              4.9   67938.0           72097.015574\n",
       "16              5.1   66029.0           73987.008038\n",
       "17              5.3   83088.0           75877.000502\n",
       "18              5.9   81363.0           81546.977895\n",
       "19              6.0   93940.0           82491.974127\n",
       "20              6.8   91738.0           90051.943985\n",
       "21              7.1   98273.0           92886.932681\n",
       "22              7.9  101302.0          100446.902538\n",
       "23              8.2  113812.0          103281.891235\n",
       "24              8.7  109431.0          108006.872395\n",
       "25              9.0  105582.0          110841.861092\n",
       "26              9.5  116969.0          115566.842252\n",
       "27              9.6  112635.0          116511.838485\n",
       "28             10.3  122391.0          123126.812110\n",
       "29             10.5  121872.0          125016.804574"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = pd.DataFrame()\n",
    "predicted['YearsExperience'] = salary_data.YearsExperience\n",
    "predicted['Salary'] = salary_data.Salary\n",
    "predicted['Predicted_Salary_Hike'] = pd.DataFrame(linear_model.predict(predicted.YearsExperience))\n",
    "predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1136615c",
   "metadata": {},
   "source": [
    "# The End !!!"
   ]
  }
 ],
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