HispanicOVR_LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries \n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12806, 61)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset\n",
    "\n",
    "dataset = pd.read_csv('Mapping_Police_Violence.csv')\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with 'Unknown race': 1247\n"
     ]
    }
   ],
   "source": [
    "unknown_race_count = dataset['race'].value_counts()['Unknown race']\n",
    "print(\"Number of rows with 'Unknown race':\", unknown_race_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated DataFrame shape after removing rows with 'Unknown race': (11559, 61)\n"
     ]
    }
   ],
   "source": [
    "# Filter rows where race is not 'Unknown race'\n",
    "dataset = dataset[dataset['race'] != 'Unknown race']\n",
    "\n",
    "# Print the updated DataFrame shape\n",
    "print(\"Updated DataFrame shape after removing rows with 'Unknown race':\", dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11559, 21)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing columns that are not required\n",
    "\n",
    "\n",
    "columns_to_remove = ['victim_image', 'street_address','city','county','zip','ori','news_urls','call_for_service','tract','urban_rural_uspsai',\n",
    "                     'wapo_id','fe_id','officer_names','officer_known_past_shootings','urban_rural_nchs','hhincome_median_census_tract',\n",
    "                     'latitude','longitude','pop_total_census_tract','pop_white_census_tract','pop_black_census_tract','pop_native_american_census_tract',\n",
    "                     'pop_asian_census_tract','pop_pacific_islander_census_tract','pop_other_multiple_census_tract','pop_hispanic_census_tract',\n",
    "                     'congressional_district_113','congressperson_lastname','congressperson_firstname','congressperson_party','prosecutor_head',\n",
    "                     'prosecutor_party','prosecutor_term','prosecutor_in_court','prosecutor_special','independent_investigation',\n",
    "                     'prosecutor_url','disposition_official','officer_charged','geography']\n",
    "dataset.drop(columns=columns_to_remove,inplace=True)\n",
    "# columns_to_remove = ['victim_image', 'street_address','city','county','zip','ori','news_urls','call_for_service','tract','urban_rural_uspsai',\n",
    "#                      'wapo_id','fe_id','officer_names','officer_races','officer_known_past_shootings','urban_rural_nchs','hhincome_median_census_tract',\n",
    "#                      'latitude','longitude','pop_total_census_tract','pop_white_census_tract','pop_black_census_tract','pop_native_american_census_tract',\n",
    "#                      'pop_asian_census_tract','pop_pacific_islander_census_tract','pop_other_multiple_census_tract','pop_hispanic_census_tract',\n",
    "#                      'congressional_district_113','congressperson_lastname','congressperson_firstname','congressperson_party','prosecutor_head',\n",
    "#                      'prosecutor_race','prosecutor_gender','prosecutor_party','prosecutor_term','prosecutor_in_court','prosecutor_special','independent_investigation',\n",
    "#                      'prosecutor_url','disposition_official','officer_charged','off_duty_killing','geography']\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "removing dae, circumsances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming your dataset is stored in a DataFrame called 'data'\n",
    "# Drop the 'date' and 'circumstances' columns\n",
    "dataset.drop(['date', 'circumstances', 'wapo_body_camera', 'initial_reason', 'agency_responsible', 'name'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>state</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>signs_of_mental_illness</th>\n",
       "      <th>allegedly_armed</th>\n",
       "      <th>wapo_armed</th>\n",
       "      <th>wapo_threat_level</th>\n",
       "      <th>wapo_flee</th>\n",
       "      <th>off_duty_killing</th>\n",
       "      <th>encounter_type</th>\n",
       "      <th>officer_races</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>SC</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Knife</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mental Health/Welfare Check</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unarmed/Did Not Have Actual Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sudden Threatening Movement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Native American</td>\n",
       "      <td>ID</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unarmed/Did Not Have Actual Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Car, Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>KY</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unclear</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None/Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>MN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Pipe</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Knife</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>AR</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Drug or Alcohol Use</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Used Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>WI</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>CO</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>saw blade</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mental Health/Welfare Check</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>MO</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Hammer</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Sword</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>IL</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Used Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age gender             race state cause_of_death signs_of_mental_illness  \\\n",
       "2   62.0   Male            White    SC        Gunshot                     Yes   \n",
       "3   25.0   Male            White    TN        Gunshot                     Yes   \n",
       "4   30.0   Male            Black    TN        Gunshot                      No   \n",
       "6   39.0   Male  Native American    ID        Gunshot                      No   \n",
       "7   42.0   Male            White    KY        Gunshot                      No   \n",
       "8   67.0   Male            White    MN        Gunshot                      No   \n",
       "9   31.0   Male         Hispanic    TX        Gunshot                     Yes   \n",
       "11  36.0   Male            White    AR        Gunshot     Drug or Alcohol Use   \n",
       "14   NaN   Male            Black    TX        Gunshot                      No   \n",
       "15  52.0   Male            White    WI        Gunshot                      No   \n",
       "16  32.0   Male            Black    CO        Gunshot                 Unknown   \n",
       "17   NaN   Male         Hispanic    TX        Gunshot                     NaN   \n",
       "18  42.0   Male            White    CA        Gunshot                      No   \n",
       "19  36.0   Male         Hispanic    CA        Gunshot                     Yes   \n",
       "20  37.0   Male            White    MO        Gunshot                     Yes   \n",
       "21  43.0   Male         Hispanic    TX        Gunshot                      No   \n",
       "22  54.0   Male            White    FL        Gunshot                      No   \n",
       "23  23.0   Male            Black    AZ        Gunshot                     Yes   \n",
       "24  50.0   Male         Hispanic    CA        Gunshot                      No   \n",
       "25  37.0   Male            Black    IL        Gunshot                      No   \n",
       "\n",
       "                       allegedly_armed wapo_armed  \\\n",
       "2                      Allegedly Armed        NaN   \n",
       "3                      Allegedly Armed      Knife   \n",
       "4   Unarmed/Did Not Have Actual Weapon        NaN   \n",
       "6   Unarmed/Did Not Have Actual Weapon        NaN   \n",
       "7                              Unclear        NaN   \n",
       "8                      Allegedly Armed       Pipe   \n",
       "9                      Allegedly Armed      Knife   \n",
       "11                     Allegedly Armed        NaN   \n",
       "14                     Allegedly Armed        NaN   \n",
       "15                     Allegedly Armed        NaN   \n",
       "16                     Allegedly Armed        NaN   \n",
       "17                     Allegedly Armed        NaN   \n",
       "18                     Allegedly Armed        NaN   \n",
       "19                     Allegedly Armed  saw blade   \n",
       "20                     Allegedly Armed     Hammer   \n",
       "21                     Allegedly Armed        NaN   \n",
       "22                     Allegedly Armed        NaN   \n",
       "23                     Allegedly Armed      Sword   \n",
       "24                     Allegedly Armed        NaN   \n",
       "25                     Allegedly Armed        NaN   \n",
       "\n",
       "              wapo_threat_level    wapo_flee off_duty_killing  \\\n",
       "2             Brandished Weapon          Car              NaN   \n",
       "3             Brandished Weapon  Not Fleeing              NaN   \n",
       "4   Sudden Threatening Movement          NaN              NaN   \n",
       "6                          None    Car, Foot              NaN   \n",
       "7                  Undetermined  Not Fleeing              NaN   \n",
       "8             Brandished Weapon  Not Fleeing              NaN   \n",
       "9                        Attack  Not Fleeing              NaN   \n",
       "11            Brandished Weapon  Not Fleeing              NaN   \n",
       "14                  Used Weapon  Not Fleeing              NaN   \n",
       "15            Brandished Weapon  Not Fleeing              NaN   \n",
       "16                       Attack  Not Fleeing              NaN   \n",
       "17                       Attack         Foot              NaN   \n",
       "18            Brandished Weapon         Foot              NaN   \n",
       "19            Brandished Weapon         Foot              NaN   \n",
       "20            Brandished Weapon  Not Fleeing              NaN   \n",
       "21            Brandished Weapon  Not Fleeing              NaN   \n",
       "22            Brandished Weapon          Car              NaN   \n",
       "23            Brandished Weapon  Not Fleeing              NaN   \n",
       "24            Brandished Weapon         Foot              NaN   \n",
       "25                  Used Weapon  Not Fleeing              NaN   \n",
       "\n",
       "                 encounter_type officer_races prosecutor_race  \\\n",
       "2                  Traffic Stop           NaN             NaN   \n",
       "3   Mental Health/Welfare Check           NaN             NaN   \n",
       "4                  Traffic Stop           NaN             NaN   \n",
       "6     Other Non-Violent Offense           NaN             NaN   \n",
       "7                  None/Unknown           NaN             NaN   \n",
       "8                  Traffic Stop           NaN             NaN   \n",
       "9          Part 1 Violent Crime           NaN             NaN   \n",
       "11         Person with a Weapon           NaN             NaN   \n",
       "14         Part 1 Violent Crime           NaN             NaN   \n",
       "15         Person with a Weapon           NaN             NaN   \n",
       "16                 Traffic Stop           NaN             NaN   \n",
       "17         Part 1 Violent Crime           NaN             NaN   \n",
       "18                 Traffic Stop           NaN             NaN   \n",
       "19  Mental Health/Welfare Check           NaN             NaN   \n",
       "20    Other Non-Violent Offense           NaN             NaN   \n",
       "21    Other Non-Violent Offense           NaN             NaN   \n",
       "22         Part 1 Violent Crime           NaN             NaN   \n",
       "23         Person with a Weapon           NaN             NaN   \n",
       "24         Person with a Weapon           NaN             NaN   \n",
       "25         Part 1 Violent Crime           NaN             NaN   \n",
       "\n",
       "   prosecutor_gender  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "11               NaN  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16               NaN  \n",
       "17               NaN  \n",
       "18               NaN  \n",
       "19               NaN  \n",
       "20               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "23               NaN  \n",
       "24               NaN  \n",
       "25               NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: ['age', 'gender', 'race', 'cause_of_death', 'signs_of_mental_illness', 'allegedly_armed', 'wapo_armed', 'wapo_threat_level', 'wapo_flee', 'off_duty_killing', 'encounter_type', 'officer_races', 'prosecutor_race', 'prosecutor_gender']\n"
     ]
    }
   ],
   "source": [
    "# Check for NaN values in each column\n",
    "columns_with_nan = dataset.columns[dataset.isna().any()].tolist()\n",
    "\n",
    "# Print columns with NaN values\n",
    "print(\"Columns with NaN values:\", columns_with_nan)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Replacing NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas library\n",
    "import pandas as pd\n",
    "\n",
    "# Replace NaN values with appropriate strategies\n",
    "dataset['age'].fillna(dataset['age'].median(), inplace=True)\n",
    "dataset['race'].fillna(dataset['race'].mode()[0], inplace=True)\n",
    "dataset['cause_of_death'].fillna(dataset['cause_of_death'].mode()[0], inplace=True)\n",
    "dataset['signs_of_mental_illness'].fillna(dataset['signs_of_mental_illness'].mode()[0], inplace=True)\n",
    "dataset['wapo_flee'].fillna(dataset['wapo_flee'].mode()[0], inplace=True)\n",
    "dataset['off_duty_killing'].fillna('Not known', inplace=True)\n",
    "dataset['encounter_type'].fillna(dataset['encounter_type'].mode()[0], inplace=True)\n",
    "dataset['prosecutor_race'].fillna(dataset['prosecutor_race'].mode()[0], inplace=True)\n",
    "dataset['prosecutor_gender'].fillna(dataset['prosecutor_gender'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Analyzing redundant classes of columns for cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'age':\n",
      "[ 62.  25.  30.  39.  42.  67.  31.  36.  35.  52.  32.  37.  43.  54.\n",
      "  23.  50.  28.  58.  69.  34.  40.  26.  41.  53.  85.  44.  51.  63.\n",
      "  33.  29.  46.  55.  27.  45.  19.  49.  65.  17.  47.  59.  18.  38.\n",
      "  75.  24.  48.  81.  73.  66.  61.  16.  56.  20.  10.  12.  21.  57.\n",
      "  60.  22.   5.  78.  79.  68.  64.  13.  15.  71.  82.  88.  14.  86.\n",
      "  76.  77.  83.  72.  70.  74.   1.   2.  84.  80.   8.  91.  11.  92.\n",
      "   6.   7.   9.  95.  93. 107.  89.]\n",
      "\n",
      "Unique values in column 'state':\n",
      "['SC' 'TN' 'ID' 'KY' 'MN' 'TX' 'AR' 'WI' 'CO' 'CA' 'MO' 'FL' 'AZ' 'IL'\n",
      " 'NC' 'NY' 'GA' 'MT' 'PA' 'OH' 'MS' 'NM' 'NV' 'WA' 'KS' 'MI' 'WY' 'LA'\n",
      " 'NE' 'AL' 'NJ' 'CT' 'MD' 'ME' 'HI' 'IN' 'DC' 'NH' 'VA' 'UT' 'OR' 'IA'\n",
      " 'MA' 'AK' 'OK' 'WV' 'DE' 'ND' 'RI' 'VT' 'SD']\n",
      "\n",
      "Unique values in column 'signs_of_mental_illness':\n",
      "['Yes' 'No' 'Drug or Alcohol Use' 'Unknown'\n",
      " 'History of Drug or Alcohol Use']\n",
      "\n",
      "Unique values in column 'wapo_flee':\n",
      "['Car' 'Not Fleeing' 'Car, Foot' 'Foot' 'Other' 'Foot, Car' 'Motorcycle'\n",
      " 'bicycle' 'Motorcycle, Foot' 'tractor' 'ATV']\n",
      "\n",
      "Unique values in column 'prosecutor_race':\n",
      "['White' 'Black' 'Hispanic' 'Pacific Islander' 'Asian' 'White, White']\n",
      "\n",
      "Unique values in column 'prosecutor_gender':\n",
      "['Male' 'Female' 'Female, Male']\n",
      "\n",
      "Unique values in column 'race':\n",
      "['White' 'Black' 'Native American' 'Hispanic' 'Asian'\n",
      " 'Native Hawaiian and Pacific Islander']\n",
      "\n",
      "Unique values in column 'off_duty_killing':\n",
      "['Not known' 'Off-Duty']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns_to_check = ['age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'prosecutor_gender', 'race', 'off_duty_killing']\n",
    "\n",
    "for column in columns_to_check:\n",
    "    unique_values = dataset[column].unique()\n",
    "    print(f\"Unique values in column '{column}':\")\n",
    "    print(unique_values)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing signs_of_mental_illness column.<br>\n",
    "-> Combining 'Drug or Alcohol Use' and 'History of Drug or Alcohol Use' into single column<br>\n",
    "-> Replacing unknown with mode values<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'signs_of_mental_illness' after fixing:\n",
      "['Yes' 'No' 'Drug or Alcohol Use']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'Drug or Alcohol Use' and 'History of Drug or Alcohol Use' with 'Drug or Alcohol Use'\n",
    "dataset['signs_of_mental_illness'].replace({'Drug or Alcohol Use': 'Drug or Alcohol Use',\n",
    "                                            'History of Drug or Alcohol Use': 'Drug or Alcohol Use'}, inplace=True)\n",
    "\n",
    "# Replace 'Unknown' with the mode value (most frequent value)\n",
    "mode_value = dataset['signs_of_mental_illness'].mode()[0]\n",
    "dataset['signs_of_mental_illness'].replace({'Unknown': mode_value}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'signs_of_mental_illness' after fixing:\")\n",
    "print(dataset['signs_of_mental_illness'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing 'wapo_flee' column.<br>\n",
    "-> 'Car, foot' to Car<br>\n",
    "-> 'Foot,Car' to Car<br>\n",
    "-> 'Motorcycle,Foot' to Motorcycle<br>\n",
    "-> and other to the mode<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'wapo_flee' after fixing:\n",
      "['Car' 'Not Fleeing' 'Foot' 'Motorcycle' 'bicycle' 'tractor' 'ATV']\n"
     ]
    }
   ],
   "source": [
    "# Replace specified combinations of values\n",
    "dataset['wapo_flee'].replace({'Car, Foot': 'Car', 'Foot, Car': 'Car', 'Motorcycle, Foot': 'Motorcycle'}, inplace=True)\n",
    "\n",
    "# Replace 'Other' with the mode value\n",
    "mode_value = dataset['wapo_flee'].mode()[0]\n",
    "dataset['wapo_flee'].replace({'Other': mode_value}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'wapo_flee' after fixing:\")\n",
    "print(dataset['wapo_flee'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing 'prosecutor_race' column: <br> -> by replacing 'White, White' with White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'prosecutor_race' after fixing:\n",
      "['White' 'Black' 'Hispanic' 'Pacific Islander' 'Asian']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'White, White' with 'White'\n",
    "dataset['prosecutor_race'].replace({'White, White': 'White'}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'prosecutor_race' after fixing:\")\n",
    "print(dataset['prosecutor_race'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing race column: <br> \n",
    "-> changing 'Native Hawaiian and Pacific Islander' to Pacific Islander "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'race' after fixing:\n",
      "['White' 'Black' 'Native American' 'Hispanic' 'Asian' 'Pacific Islander']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'Native Hawaiian and Pacific Islander' with 'Pacific Islander'\n",
    "dataset['race'].replace({'Native Hawaiian and Pacific Islander': 'Pacific Islander'}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'race' after fixing:\")\n",
    "print(dataset['race'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Encoding string based columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Apply label encoding and add 1 to the encoded values\n",
    "dataset['signs_of_mental_illness'] = label_encoder.fit_transform(dataset['signs_of_mental_illness']) + 1\n",
    "dataset['allegedly_armed'] = label_encoder.fit_transform(dataset['allegedly_armed']) + 1\n",
    "dataset['wapo_armed'] = label_encoder.fit_transform(dataset['wapo_armed']) + 1\n",
    "dataset['wapo_threat_level'] = label_encoder.fit_transform(dataset['wapo_threat_level']) + 1\n",
    "dataset['wapo_flee'] = label_encoder.fit_transform(dataset['wapo_flee']) + 1\n",
    "dataset['off_duty_killing'] = label_encoder.fit_transform(dataset['off_duty_killing']) + 1\n",
    "dataset['encounter_type'] = label_encoder.fit_transform(dataset['encounter_type']) + 1\n",
    "\n",
    "dataset['officer_races'] = label_encoder.fit_transform(dataset['officer_races']) + 1\n",
    "dataset['cause_of_death'] = label_encoder.fit_transform(dataset['cause_of_death']) + 1\n",
    "dataset['prosecutor_gender'] = label_encoder.fit_transform(dataset['prosecutor_gender']) + 1\n",
    "dataset['prosecutor_race'] = label_encoder.fit_transform(dataset['prosecutor_race']) + 1\n",
    "\n",
    "dataset['gender'] = label_encoder.fit_transform(dataset['gender']) + 1\n",
    "dataset['state'] = label_encoder.fit_transform(dataset['state']) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>state</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>signs_of_mental_illness</th>\n",
       "      <th>allegedly_armed</th>\n",
       "      <th>wapo_armed</th>\n",
       "      <th>wapo_threat_level</th>\n",
       "      <th>wapo_flee</th>\n",
       "      <th>off_duty_killing</th>\n",
       "      <th>encounter_type</th>\n",
       "      <th>officer_races</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Black</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Native American</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>213</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>213</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Black</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2</td>\n",
       "      <td>White</td>\n",
       "      <td>49</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender             race  state  cause_of_death  \\\n",
       "2   62.0       2            White     41              19   \n",
       "3   25.0       2            White     43              19   \n",
       "4   30.0       2            Black     43              19   \n",
       "6   39.0       2  Native American     14              19   \n",
       "7   42.0       2            White     18              19   \n",
       "8   67.0       2            White     24              19   \n",
       "9   31.0       2         Hispanic     44              19   \n",
       "11  36.0       2            White      3              19   \n",
       "14  35.0       2            Black     44              19   \n",
       "15  52.0       2            White     49              19   \n",
       "\n",
       "    signs_of_mental_illness  allegedly_armed  wapo_armed  wapo_threat_level  \\\n",
       "2                         3                1         213                  2   \n",
       "3                         3                1          96                  2   \n",
       "4                         2                2         213                  5   \n",
       "6                         2                2         213                  3   \n",
       "7                         2                3         213                  7   \n",
       "8                         2                1         145                  2   \n",
       "9                         3                1          96                  1   \n",
       "11                        1                1         213                  2   \n",
       "14                        2                1         213                  8   \n",
       "15                        2                1         213                  2   \n",
       "\n",
       "    wapo_flee  off_duty_killing  encounter_type  officer_races  \\\n",
       "2           2                 1              21             43   \n",
       "3           5                 1               6             43   \n",
       "4           5                 1              21             43   \n",
       "6           2                 1              10             43   \n",
       "7           5                 1               7             43   \n",
       "8           5                 1              21             43   \n",
       "9           5                 1              11             43   \n",
       "11          5                 1              16             43   \n",
       "14          5                 1              11             43   \n",
       "15          5                 1              16             43   \n",
       "\n",
       "    prosecutor_race  prosecutor_gender  \n",
       "2                 5                  3  \n",
       "3                 5                  3  \n",
       "4                 5                  3  \n",
       "6                 5                  3  \n",
       "7                 5                  3  \n",
       "8                 5                  3  \n",
       "9                 5                  3  \n",
       "11                5                  3  \n",
       "14                5                  3  \n",
       "15                5                  3  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Preparing race column for one vs rest comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset['race_binary'] = dataset['race'].apply(lambda x: 1 if x.lower() == 'hispanic' else 0)\n",
    "# dataset.drop(columns=['race'], inplace=True)\n",
    "# dataset.rename(columns={'race_binary': 'race'}, inplace=True)\n",
    "\n",
    "df = dataset.copy()\n",
    "\n",
    "df['race_binary'] = dataset['race'].apply(lambda x: 1 if x.lower() == 'hispanic' else 0)\n",
    "df.drop(columns=['race'], inplace=True)\n",
    "df.rename(columns={'race_binary': 'race'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Analyzing correlations between 'race' and other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wt/07csprcj0b79dwm10ts4hh1m0000gn/T/ipykernel_14957/3569804389.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = dataset.corr()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = dataset.corr()\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Train-Test Split<br>\n",
    "<h3>The variables we choose are: <br> <h4><i>'age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'gender','wapo_threat_level', 'off_duty_killing'.<i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9247, 8), (2312, 8), (9247,), (2312,))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'gender','wapo_threat_level', 'off_duty_killing']]\n",
    "y = df['race']\n",
    "\n",
    "# Split the datasetset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Running logistic regression to train our model in predicting Hispanic race incidents\n",
    "<h4><i>OVR strategy<i><h4> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8083910034602076\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "clf = LogisticRegression(multi_class='ovr', solver='liblinear')\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test.values, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      1.00      0.89      1869\n",
      "           1       0.00      0.00      0.00       443\n",
      "\n",
      "    accuracy                           0.81      2312\n",
      "   macro avg       0.40      0.50      0.45      2312\n",
      "weighted avg       0.65      0.81      0.72      2312\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ajm/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/ajm/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/ajm/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm_df = pd.DataFrame(cm, index=[i for i in np.unique(y_test)], columns=[i for i in np.unique(y_test)])\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(cm_df, annot=True, fmt='g')\n",
    "\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(\"\\nClassification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "y_pred_proba = clf.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr, tpr, label=\"AUC=\"+str(auc))\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.legend(loc=4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Initial Experiment Results </h1>\n",
    "\n",
    "<p> For the initial experiment, we gave a value of 1 to the hispanic race and 0 to all other races. Initially, we attained a decent accuracy score with this model which means that the model correctly predicted the race for approximately 80.84% of the test dataset. However, this does not give us the complete story, which brings us to other metrics for performance. The confusion matrix in the second image shows that there were 1869 correct predictions for class '0' and 443 correct predictions for class '1'. There are no off-diagonal elements, which means there were no instances where class '0' was mistaken for class '1', or vice versa. This seems quite unusual for a real-world dataset. Our inference is that this occurs to the imbalances in the data. This is because the model seems to perform well for class '0' but fails completely for class '1'. This is evident from the classification report where class '1' has zero precision and recall. Moreover, the absence of any predicted instances for class '1' suggests that the model might be biased towards class '0' which is dominated with the \"White\" race. Given the complete lack of predictions for class '1', we can infer that the dataset is systematically structured to underrepresent minorities and overrepresent the White race.</p>\n",
    "\n",
    "<h5>Understanding the ROC Curve and AUC Calculation</h5>\n",
    "\n",
    "<p> <b> ROC Curve: </b>The ROC curve seems to rise slowly towards the True Positive Rate (TPR) of 1, which suggests that in order to correctly predict the minority class (class '1'), the model also increases the False Positive Rate (FPR) which means that it is struggling to separate the two classes without making a significant number of false positive errors. Considering what we saw earlier in the confusion matrix, which showed perfect recall for class '0' but none for class '1', we can draw the following conclusions: </p>\n",
    "\n",
    "<li>The current model is capable of identifying the majority class '0' quite well, but at the expense of incorrectly classifying many instances of the minority class '1' as the majority class.</li>\n",
    "<li>There's a significant imbalance in model performance across classes, with the model heavily favoring the majority class.</li>\n",
    "<li>We need to address the class imbalance.</li>\n",
    "\n",
    "<p> <b> AUC Score: </b>The AUC score is approximately 0.6027 which is marginally better than a random guess, which would have an AUC of 0.5. For the reader's reference, an AUC of 1 represents a perfect model, while an AUC closer to 0.5 suggests that the model has limited ability to distinguish between the classes. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Next Experiment:  SMOTE (Synthetic Minority Over-sampling Technique) </h1>\n",
    "\n",
    "<h5> While we achieved a high accuracy for the initial experiment, it is not a good indicator of performance for the aforementioned reasons. In this experiment, we will be using SMOTE to mitigate class imbalances and re-evaluate our logistic regression model with our revised balanced dataset. Moreover, we will be using the 'saga' solver, or the Stochastic Average Gradient descent with Augmented factorization and Intrinsic momentum. It's a variation of the sag solver that also supports the non-smooth penalty='l1' option which we have commonly referred to in class as the L1 Lasso Regularization technique. Given our dataset, this is the best choice as it handles sparse multinomial logistic regression effectively and is very fast for datasets of considerable size.</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5791522491349481\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Create an instance of SMOTE\n",
    "smote = SMOTE(random_state=65)\n",
    "\n",
    "# Fit SMOTE on the training data\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "clf2 = LogisticRegression(multi_class='ovr', solver='saga', penalty='l1', max_iter=10000)\n",
    "clf2.fit(X_train_smote, y_train_smote)\n",
    "y_pred2 = clf2.predict(X_test)\n",
    "\n",
    "accuracy2 = accuracy_score(y_test.values, y_pred2)\n",
    "print(\"Accuracy:\", accuracy2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.59      0.69      1869\n",
      "           1       0.24      0.55      0.33       443\n",
      "\n",
      "    accuracy                           0.58      2312\n",
      "   macro avg       0.54      0.57      0.51      2312\n",
      "weighted avg       0.73      0.58      0.62      2312\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_pred2)\n",
    "cm_df2 = pd.DataFrame(cm2, index=[i for i in np.unique(y_test)], columns=[i for i in np.unique(y_test)])\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(cm_df2, annot=True, fmt='g')\n",
    "\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "report2 = classification_report(y_test, y_pred2)\n",
    "print(\"\\nClassification Report:\\n\", report2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "y_pred_proba2 = clf2.predict_proba(X_test)[::,1]\n",
    "\n",
    "fpr2, tpr2, _ = roc_curve(y_test, y_pred_proba2)\n",
    "\n",
    "auc2 = roc_auc_score(y_test, y_pred_proba2)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "plt.plot(fpr2, tpr2, label=\"Experiment 2 AUC=\"+str(auc2))\n",
    "\n",
    "plt.title('ROC Curve for Experiment 2')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.legend(loc=4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Experiment 2 Results </h1>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>\n",
    "\n",
    "Given the current data and configuration, we were able to make the saga solver converge with increased iterations and finetune the model's parameters to their optimal point as much as we could. \n",
    "\n",
    "In the initial experiment without SMOTE, an 80% accuracy was an unreliable metric. Now, the accuracy is approximately 0.5795. While this is lower than the first experiment, it may now be a more meaningful metric since the class imbalance has been addressed. Nonetheless, the model is now actually recognizing instances of the minority class, whereas it previously predicted none. The recall for the minority Hispanic class has substantially improved (from 0 to 0.55), indicating that the model's sensitivity to the minority class is much better, even though the precision for this class is low. Moreover, the AUC has decreased slightly (from 0.6027 to 0.5944), which may suggest that the model's ability to distinguish between classes is not markedly improved, but it is now actually attempting to do so for both classes.\n",
    "\n",
    "Overall, using SMOTE to address class imbalance has led to a classifier that is more balanced in terms of its sensitivity to both classes, which is a good outcome. Furthermore, the lowered accuracy and AUC in the context of a balanced dataset indicate that while the model may not perform as well overall according to these metrics, it is now a more fair and balanced classifier. The key highlight, however, of this experiment is that we were successfully able to increase the recall for the Hispanic minority class, which is a critical improvement in the direction of detecting police violence against minority classes like Hispanics. While it is true that this improvement came at the expense of making more false positive errors for this class, it is important to take into consideration that we began with a dataset that was systematically structured to be biased towards the White race.</h5>\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}