source/ML/Final_Logistic_Regression_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8946fc78",
   "metadata": {},
   "source": [
    "# Logistic Regression Model \n",
    "\n",
    "Trained and tested a logistic regression model to predict recession from the interest rate difference in long term and short term treasury securities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e3e77c29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEA_Base_For_ML_Model completed - gdp_df ready for use\n"
     ]
    }
   ],
   "source": [
    "# Run the BEA base Jupyter Notebook\n",
    "\n",
    "%run BEA_Base_For_ML_Model.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "11be2845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Treasury_Rates_Base_For_ML completed. treasury_df is ready for use \n"
     ]
    }
   ],
   "source": [
    "# Run the Treasury base Jupyter Notebook\n",
    "\n",
    "%run Treasury_Rates_Base_For_ML.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3a30805a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "82852ccd",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Key Metrics</th>\n",
       "      <th>Year-Qtr</th>\n",
       "      <th>Gross domestic product</th>\n",
       "      <th>Personal consumption expenditures</th>\n",
       "      <th>Goods</th>\n",
       "      <th>Durable goods</th>\n",
       "      <th>Motor vehicles and parts</th>\n",
       "      <th>Furnishings and durable household equipment</th>\n",
       "      <th>Recreational goods and vehicles</th>\n",
       "      <th>Other durable goods</th>\n",
       "      <th>Nondurable goods</th>\n",
       "      <th>...</th>\n",
       "      <th>Fed- National Defense - Consumption expenditures</th>\n",
       "      <th>Fed- National Defense - Gross Investment</th>\n",
       "      <th>Nondefense</th>\n",
       "      <th>Fed - Nondefense - Consumption expenditures</th>\n",
       "      <th>Fed - Nondefense - Gross Investment</th>\n",
       "      <th>State and local</th>\n",
       "      <th>State and local - Consumption expenditures</th>\n",
       "      <th>State and local - Gross Investment</th>\n",
       "      <th>GDP_Growth</th>\n",
       "      <th>Is_Recession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959Q1</td>\n",
       "      <td>510.330</td>\n",
       "      <td>309.449</td>\n",
       "      <td>169.723</td>\n",
       "      <td>43.649</td>\n",
       "      <td>18.090</td>\n",
       "      <td>15.143</td>\n",
       "      <td>6.290</td>\n",
       "      <td>4.125</td>\n",
       "      <td>126.074</td>\n",
       "      <td>...</td>\n",
       "      <td>42.349</td>\n",
       "      <td>18.787</td>\n",
       "      <td>11.230</td>\n",
       "      <td>8.456</td>\n",
       "      <td>2.774</td>\n",
       "      <td>44.829</td>\n",
       "      <td>30.172</td>\n",
       "      <td>14.656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959Q2</td>\n",
       "      <td>522.653</td>\n",
       "      <td>315.505</td>\n",
       "      <td>172.617</td>\n",
       "      <td>45.465</td>\n",
       "      <td>19.335</td>\n",
       "      <td>15.548</td>\n",
       "      <td>6.374</td>\n",
       "      <td>4.208</td>\n",
       "      <td>127.152</td>\n",
       "      <td>...</td>\n",
       "      <td>41.394</td>\n",
       "      <td>18.895</td>\n",
       "      <td>13.290</td>\n",
       "      <td>10.441</td>\n",
       "      <td>2.849</td>\n",
       "      <td>44.955</td>\n",
       "      <td>30.541</td>\n",
       "      <td>14.414</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959Q3</td>\n",
       "      <td>525.034</td>\n",
       "      <td>320.725</td>\n",
       "      <td>174.524</td>\n",
       "      <td>46.336</td>\n",
       "      <td>20.073</td>\n",
       "      <td>15.647</td>\n",
       "      <td>6.392</td>\n",
       "      <td>4.224</td>\n",
       "      <td>128.188</td>\n",
       "      <td>...</td>\n",
       "      <td>42.051</td>\n",
       "      <td>19.347</td>\n",
       "      <td>13.134</td>\n",
       "      <td>10.339</td>\n",
       "      <td>2.795</td>\n",
       "      <td>44.960</td>\n",
       "      <td>30.964</td>\n",
       "      <td>13.996</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959Q4</td>\n",
       "      <td>528.600</td>\n",
       "      <td>322.842</td>\n",
       "      <td>173.564</td>\n",
       "      <td>44.103</td>\n",
       "      <td>17.658</td>\n",
       "      <td>15.733</td>\n",
       "      <td>6.420</td>\n",
       "      <td>4.293</td>\n",
       "      <td>129.461</td>\n",
       "      <td>...</td>\n",
       "      <td>42.233</td>\n",
       "      <td>18.722</td>\n",
       "      <td>12.880</td>\n",
       "      <td>9.942</td>\n",
       "      <td>2.938</td>\n",
       "      <td>44.772</td>\n",
       "      <td>31.494</td>\n",
       "      <td>13.278</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960Q1</td>\n",
       "      <td>542.648</td>\n",
       "      <td>326.364</td>\n",
       "      <td>175.060</td>\n",
       "      <td>45.455</td>\n",
       "      <td>19.343</td>\n",
       "      <td>15.522</td>\n",
       "      <td>6.359</td>\n",
       "      <td>4.232</td>\n",
       "      <td>129.605</td>\n",
       "      <td>...</td>\n",
       "      <td>41.592</td>\n",
       "      <td>18.432</td>\n",
       "      <td>10.957</td>\n",
       "      <td>7.822</td>\n",
       "      <td>3.135</td>\n",
       "      <td>45.970</td>\n",
       "      <td>32.436</td>\n",
       "      <td>13.534</td>\n",
       "      <td>0.026576</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Key Metrics Year-Qtr  Gross domestic product  \\\n",
       "0             1959Q1                 510.330   \n",
       "1             1959Q2                 522.653   \n",
       "2             1959Q3                 525.034   \n",
       "3             1959Q4                 528.600   \n",
       "4             1960Q1                 542.648   \n",
       "\n",
       "Key Metrics  Personal consumption expenditures    Goods  Durable goods  \\\n",
       "0                                      309.449  169.723         43.649   \n",
       "1                                      315.505  172.617         45.465   \n",
       "2                                      320.725  174.524         46.336   \n",
       "3                                      322.842  173.564         44.103   \n",
       "4                                      326.364  175.060         45.455   \n",
       "\n",
       "Key Metrics  Motor vehicles and parts  \\\n",
       "0                              18.090   \n",
       "1                              19.335   \n",
       "2                              20.073   \n",
       "3                              17.658   \n",
       "4                              19.343   \n",
       "\n",
       "Key Metrics  Furnishings and durable household equipment  \\\n",
       "0                                                 15.143   \n",
       "1                                                 15.548   \n",
       "2                                                 15.647   \n",
       "3                                                 15.733   \n",
       "4                                                 15.522   \n",
       "\n",
       "Key Metrics  Recreational goods and vehicles  Other durable goods  \\\n",
       "0                                      6.290                4.125   \n",
       "1                                      6.374                4.208   \n",
       "2                                      6.392                4.224   \n",
       "3                                      6.420                4.293   \n",
       "4                                      6.359                4.232   \n",
       "\n",
       "Key Metrics  Nondurable goods  ...  \\\n",
       "0                     126.074  ...   \n",
       "1                     127.152  ...   \n",
       "2                     128.188  ...   \n",
       "3                     129.461  ...   \n",
       "4                     129.605  ...   \n",
       "\n",
       "Key Metrics  Fed- National Defense - Consumption expenditures  \\\n",
       "0                                                      42.349   \n",
       "1                                                      41.394   \n",
       "2                                                      42.051   \n",
       "3                                                      42.233   \n",
       "4                                                      41.592   \n",
       "\n",
       "Key Metrics  Fed- National Defense - Gross Investment  Nondefense  \\\n",
       "0                                              18.787      11.230   \n",
       "1                                              18.895      13.290   \n",
       "2                                              19.347      13.134   \n",
       "3                                              18.722      12.880   \n",
       "4                                              18.432      10.957   \n",
       "\n",
       "Key Metrics  Fed - Nondefense - Consumption expenditures  \\\n",
       "0                                                  8.456   \n",
       "1                                                 10.441   \n",
       "2                                                 10.339   \n",
       "3                                                  9.942   \n",
       "4                                                  7.822   \n",
       "\n",
       "Key Metrics  Fed - Nondefense - Gross Investment  State and local  \\\n",
       "0                                          2.774           44.829   \n",
       "1                                          2.849           44.955   \n",
       "2                                          2.795           44.960   \n",
       "3                                          2.938           44.772   \n",
       "4                                          3.135           45.970   \n",
       "\n",
       "Key Metrics  State and local - Consumption expenditures  \\\n",
       "0                                                30.172   \n",
       "1                                                30.541   \n",
       "2                                                30.964   \n",
       "3                                                31.494   \n",
       "4                                                32.436   \n",
       "\n",
       "Key Metrics  State and local - Gross Investment  GDP_Growth  Is_Recession  \n",
       "0                                        14.656    0.000000          True  \n",
       "1                                        14.414    0.024147         False  \n",
       "2                                        13.996    0.004556         False  \n",
       "3                                        13.278    0.006792         False  \n",
       "4                                        13.534    0.026576         False  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the GDP dataframe\n",
    "\n",
    "gdp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a219f319",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year-Qtr</th>\n",
       "      <th>10-2 Rate Diff</th>\n",
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       "      <th>0</th>\n",
       "      <td>1959Q1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.486667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959Q2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959Q3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959Q4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960Q1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Year-Qtr  10-2 Rate Diff  10-1 Rate Diff\n",
       "0   1959Q1             0.0        0.486667\n",
       "1   1959Q2             0.0        0.340000\n",
       "2   1959Q3             0.0       -0.100000\n",
       "3   1959Q4             0.0       -0.333333\n",
       "4   1960Q1             0.0       -0.083333"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the Treasury dataframe\n",
    "\n",
    "treasury_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "99fc419a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge GDP DF and Treasury DF\n",
    "gdp_df = pd.merge(gdp_df, treasury_df, on = \"Year-Qtr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f751dbc7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Nondurable goods</th>\n",
       "      <th>...</th>\n",
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       "      <th>Fed - Nondefense - Consumption expenditures</th>\n",
       "      <th>Fed - Nondefense - Gross Investment</th>\n",
       "      <th>State and local</th>\n",
       "      <th>State and local - Consumption expenditures</th>\n",
       "      <th>State and local - Gross Investment</th>\n",
       "      <th>GDP_Growth</th>\n",
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       "      <th>0</th>\n",
       "      <td>1959Q1</td>\n",
       "      <td>510.330</td>\n",
       "      <td>309.449</td>\n",
       "      <td>169.723</td>\n",
       "      <td>43.649</td>\n",
       "      <td>18.090</td>\n",
       "      <td>15.143</td>\n",
       "      <td>6.290</td>\n",
       "      <td>4.125</td>\n",
       "      <td>126.074</td>\n",
       "      <td>...</td>\n",
       "      <td>11.230</td>\n",
       "      <td>8.456</td>\n",
       "      <td>2.774</td>\n",
       "      <td>44.829</td>\n",
       "      <td>30.172</td>\n",
       "      <td>14.656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959Q2</td>\n",
       "      <td>522.653</td>\n",
       "      <td>315.505</td>\n",
       "      <td>172.617</td>\n",
       "      <td>45.465</td>\n",
       "      <td>19.335</td>\n",
       "      <td>15.548</td>\n",
       "      <td>6.374</td>\n",
       "      <td>4.208</td>\n",
       "      <td>127.152</td>\n",
       "      <td>...</td>\n",
       "      <td>13.290</td>\n",
       "      <td>10.441</td>\n",
       "      <td>2.849</td>\n",
       "      <td>44.955</td>\n",
       "      <td>30.541</td>\n",
       "      <td>14.414</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959Q3</td>\n",
       "      <td>525.034</td>\n",
       "      <td>320.725</td>\n",
       "      <td>174.524</td>\n",
       "      <td>46.336</td>\n",
       "      <td>20.073</td>\n",
       "      <td>15.647</td>\n",
       "      <td>6.392</td>\n",
       "      <td>4.224</td>\n",
       "      <td>128.188</td>\n",
       "      <td>...</td>\n",
       "      <td>13.134</td>\n",
       "      <td>10.339</td>\n",
       "      <td>2.795</td>\n",
       "      <td>44.960</td>\n",
       "      <td>30.964</td>\n",
       "      <td>13.996</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959Q4</td>\n",
       "      <td>528.600</td>\n",
       "      <td>322.842</td>\n",
       "      <td>173.564</td>\n",
       "      <td>44.103</td>\n",
       "      <td>17.658</td>\n",
       "      <td>15.733</td>\n",
       "      <td>6.420</td>\n",
       "      <td>4.293</td>\n",
       "      <td>129.461</td>\n",
       "      <td>...</td>\n",
       "      <td>12.880</td>\n",
       "      <td>9.942</td>\n",
       "      <td>2.938</td>\n",
       "      <td>44.772</td>\n",
       "      <td>31.494</td>\n",
       "      <td>13.278</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960Q1</td>\n",
       "      <td>542.648</td>\n",
       "      <td>326.364</td>\n",
       "      <td>175.060</td>\n",
       "      <td>45.455</td>\n",
       "      <td>19.343</td>\n",
       "      <td>15.522</td>\n",
       "      <td>6.359</td>\n",
       "      <td>4.232</td>\n",
       "      <td>129.605</td>\n",
       "      <td>...</td>\n",
       "      <td>10.957</td>\n",
       "      <td>7.822</td>\n",
       "      <td>3.135</td>\n",
       "      <td>45.970</td>\n",
       "      <td>32.436</td>\n",
       "      <td>13.534</td>\n",
       "      <td>0.026576</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Year-Qtr  Gross domestic product  Personal consumption expenditures  \\\n",
       "0   1959Q1                 510.330                            309.449   \n",
       "1   1959Q2                 522.653                            315.505   \n",
       "2   1959Q3                 525.034                            320.725   \n",
       "3   1959Q4                 528.600                            322.842   \n",
       "4   1960Q1                 542.648                            326.364   \n",
       "\n",
       "     Goods  Durable goods  Motor vehicles and parts  \\\n",
       "0  169.723         43.649                    18.090   \n",
       "1  172.617         45.465                    19.335   \n",
       "2  174.524         46.336                    20.073   \n",
       "3  173.564         44.103                    17.658   \n",
       "4  175.060         45.455                    19.343   \n",
       "\n",
       "   Furnishings and durable household equipment  \\\n",
       "0                                       15.143   \n",
       "1                                       15.548   \n",
       "2                                       15.647   \n",
       "3                                       15.733   \n",
       "4                                       15.522   \n",
       "\n",
       "   Recreational goods and vehicles  Other durable goods  Nondurable goods  \\\n",
       "0                            6.290                4.125           126.074   \n",
       "1                            6.374                4.208           127.152   \n",
       "2                            6.392                4.224           128.188   \n",
       "3                            6.420                4.293           129.461   \n",
       "4                            6.359                4.232           129.605   \n",
       "\n",
       "   ...  Nondefense  Fed - Nondefense - Consumption expenditures  \\\n",
       "0  ...      11.230                                        8.456   \n",
       "1  ...      13.290                                       10.441   \n",
       "2  ...      13.134                                       10.339   \n",
       "3  ...      12.880                                        9.942   \n",
       "4  ...      10.957                                        7.822   \n",
       "\n",
       "   Fed - Nondefense - Gross Investment  State and local  \\\n",
       "0                                2.774           44.829   \n",
       "1                                2.849           44.955   \n",
       "2                                2.795           44.960   \n",
       "3                                2.938           44.772   \n",
       "4                                3.135           45.970   \n",
       "\n",
       "   State and local - Consumption expenditures  \\\n",
       "0                                      30.172   \n",
       "1                                      30.541   \n",
       "2                                      30.964   \n",
       "3                                      31.494   \n",
       "4                                      32.436   \n",
       "\n",
       "   State and local - Gross Investment  GDP_Growth  Is_Recession  \\\n",
       "0                              14.656    0.000000          True   \n",
       "1                              14.414    0.024147         False   \n",
       "2                              13.996    0.004556         False   \n",
       "3                              13.278    0.006792         False   \n",
       "4                              13.534    0.026576         False   \n",
       "\n",
       "   10-2 Rate Diff  10-1 Rate Diff  \n",
       "0             0.0        0.486667  \n",
       "1             0.0        0.340000  \n",
       "2             0.0       -0.100000  \n",
       "3             0.0       -0.333333  \n",
       "4             0.0       -0.083333  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the merged DataFrame\n",
    "gdp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1b2f71e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_df[\"GDP_Growth\"] = gdp_df[\"GDP_Growth\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2e686e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot GDP Growth and mark recession (negative GDP Growth)\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "fig = plt.figure(figsize = (12, 7))\n",
    "\n",
    "# Store GDP Growth in a list\n",
    "\n",
    "gdp_growth = gdp_df[\"GDP_Growth\"].to_list()\n",
    "\n",
    "# Set the colors list to mark recession quarters\n",
    "\n",
    "colors = [('turquoise' if g > 0 else 'maroon') for g in gdp_growth]\n",
    "\n",
    "# Store Year - Quarter in a list\n",
    "year_qtr = gdp_df[\"Year-Qtr\"].to_list()\n",
    "\n",
    "# Define a list for storing xticks\n",
    "xticks=[]\n",
    "\n",
    "# Define a list for storing xticks labels\n",
    "xticks_labels=[]\n",
    "\n",
    "# Build xticks and xticks_labels list by iterating over year_qtr list\n",
    "for i in range(1, len(year_qtr)):\n",
    "    if i%15 == 0 or i==1:\n",
    "        sel_year_qtr = year_qtr[i]\n",
    "        xticks_labels.append(sel_year_qtr[0:4])\n",
    "        xticks.append(sel_year_qtr)\n",
    "\n",
    "# Create bar chart using matplotlib\n",
    "plt.bar(gdp_df[\"Year-Qtr\"], gdp_df[\"GDP_Growth\"], color=colors, label='GDP Growth')\n",
    "\n",
    "# Create line plot using matplotlib\n",
    "plt.plot(gdp_df[\"Year-Qtr\"], gdp_df[\"10-1 Rate Diff\"], label='10 Minus 1 T Bill Rates')\n",
    "\n",
    "# Set the title of bar chart\n",
    "\n",
    "plt.title(\"US Economic Recessions since 1959\")\n",
    "\n",
    "# Set the Ylabel\n",
    "\n",
    "plt.ylabel(\"GDP Growth & 10 Minus 1 T Bill Rates\")\n",
    "\n",
    "# Set xticks for the bar chart\n",
    "plt.xticks(xticks, xticks_labels, rotation=45)\n",
    "\n",
    "\n",
    "# Show legend in upper left\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "# Save the chart\n",
    "plt.savefig(\"../../images/economic_recessions_US_1959.png\")\n",
    "\n",
    "\n",
    "# Display the bar chart\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "788c24a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Features to predict on\n",
    "\n",
    "X = gdp_df[['10-2 Rate Diff',\n",
    "            '10-1 Rate Diff',\n",
    "            'Change in private inventories'\n",
    "           ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3af2d15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the target variable\n",
    "\n",
    "y = gdp_df[\"Is_Recession\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "478779f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the test and train datasets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d6a38a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Logistic Regression model\n",
    "\n",
    "model = LogisticRegression(solver=\"lbfgs\", random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7470b146",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7c5b2e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict values using the model\n",
    "\n",
    "predicted_y_values = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a42c88ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9807692307692307\n"
     ]
    }
   ],
   "source": [
    "# Score the model\n",
    "\n",
    "score = model.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6ef62b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9807692307692307"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the accuracy score\n",
    "\n",
    "accuracy_score(y_test, predicted_y_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "35a3b305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0],\n",
       "       [ 1,  1]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the confusion matrix\n",
    "\n",
    "confusion_matrix(y_test, predicted_y_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "06558be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.98      1.00      0.99        50\n",
      "        True       1.00      0.50      0.67         2\n",
      "\n",
      "    accuracy                           0.98        52\n",
      "   macro avg       0.99      0.75      0.83        52\n",
      "weighted avg       0.98      0.98      0.98        52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report\n",
    "\n",
    "print(classification_report(y_test, predicted_y_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696ff4d1",
   "metadata": {},
   "source": [
    "### Model Optimization\n",
    "\n",
    "The accuracy score was recorded as 95.38% for the original model. The precision and recall for the 'True' label was 0.\n",
    "\n",
    "To improve accuracy, precision, and recall, the following optimizations were done- \n",
    "* An additional feature \"Change in Private Inventories' was introduced. \n",
    "* For the train and test split, the test size was adjusted to 20%, and stratify was set to 'y'\n",
    "\n",
    "Following ML algorithms were tried out but didn't improve the score and hence wasn't considered for this model.\n",
    "\n",
    "* K-Nearest Neighbors (KNN) algorithm\n",
    "* Decision Tree\n",
    "* XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4132347",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "The optimized version of the Logistic regression model for predicting an economic recession based on differences in the interest rates of long-term and short-term treasuries and changes in private inventories achieved an accuracy score of **98.07%**. The precision and recall for ‘Is_Recession’ label ‘False’ are .98 and 1.00 respectively. The precision and recall for the ‘Is_Recession’ label ‘True’ are 1.0 and .50 respectively.\n",
    "\n",
    "### We recommend this model for predicting an economic recession.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5daa34b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5694675",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aabf5ac5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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