XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CrimeCast: Forecasting Crime Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![CrimeCast Banner](https://drive.google.com/uc?export=view&id=1eskrvAYu6lUvH6Uhyp_l1v54ykTJFDD9)\n",
    "<h3 style=\"color:red;\">About</h3>\n",
    "<p>This notebook focuses on crime forecasting using a comprehensive dataset that captures various aspects of criminal activities, including date, time, location, victim details, and more.</p>\n",
    "<br>\n",
    "<h3 style=\"color:red;\">Goal</h3>\n",
    "<p>Our goal is to leverage machine learning techniques to analyze the dataset and predict crime categories effectively.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np; import pandas as pd; pd.set_option('display.max_columns', None)\n",
    "import matplotlib.pyplot as plt; import seaborn as sns\n",
    "\n",
    "train_df = pd.read_csv('data/train.csv')\n",
    "test_df = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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>Location</th>\n",
       "      <th>Cross_Street</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Modus_Operandi</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Premise_Description</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Weapon_Description</th>\n",
       "      <th>Status</th>\n",
       "      <th>Status_Description</th>\n",
       "      <th>Crime_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500    CARPENTER                    AV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>03/09/2020 12:00:00 AM</td>\n",
       "      <td>03/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>N Hollywood</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0385</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45TH                         ST</td>\n",
       "      <td>ALAMEDA                      ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Newton</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0906 0352 0371 0446 1822 0344 0416 0417</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>SWAP MEET</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600 E  MARTIN LUTHER KING JR        BL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>605.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Newton</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0329 1202</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>SINGLE FAMILY DWELLING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900    ORO GRANDE                   ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>11/08/2020 12:00:00 AM</td>\n",
       "      <td>11/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Mission</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0329 1300</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100 S  VERMONT                      AV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>77th Street</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0416 0945 1822 0400 0417 0344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>MINI-MART</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Location                     Cross_Street  \\\n",
       "0   4500    CARPENTER                    AV                              NaN   \n",
       "1           45TH                         ST  ALAMEDA                      ST   \n",
       "2    600 E  MARTIN LUTHER KING JR        BL                              NaN   \n",
       "3  14900    ORO GRANDE                   ST                              NaN   \n",
       "4   7100 S  VERMONT                      AV                              NaN   \n",
       "\n",
       "   Latitude  Longitude           Date_Reported           Date_Occurred  \\\n",
       "0   34.1522  -118.3910  03/09/2020 12:00:00 AM  03/06/2020 12:00:00 AM   \n",
       "1   34.0028  -118.2391  02/27/2020 12:00:00 AM  02/27/2020 12:00:00 AM   \n",
       "2   34.0111  -118.2653  08/21/2020 12:00:00 AM  08/21/2020 12:00:00 AM   \n",
       "3   34.2953  -118.4590  11/08/2020 12:00:00 AM  11/06/2020 12:00:00 AM   \n",
       "4   33.9787  -118.2918  02/25/2020 12:00:00 AM  02/25/2020 12:00:00 AM   \n",
       "\n",
       "   Time_Occurred  Area_ID    Area_Name  Reporting_District_no  Part 1-2  \\\n",
       "0         1800.0     15.0  N Hollywood                 1563.0       1.0   \n",
       "1         1345.0     13.0       Newton                 1367.0       1.0   \n",
       "2          605.0     13.0       Newton                 1343.0       2.0   \n",
       "3         1800.0     19.0      Mission                 1924.0       1.0   \n",
       "4         1130.0     12.0  77th Street                 1245.0       1.0   \n",
       "\n",
       "                            Modus_Operandi  Victim_Age Victim_Sex  \\\n",
       "0                                     0385        75.0          M   \n",
       "1  0906 0352 0371 0446 1822 0344 0416 0417        41.0          M   \n",
       "2                                0329 1202        67.0          M   \n",
       "3                                0329 1300        61.0          M   \n",
       "4            0416 0945 1822 0400 0417 0344         0.0          X   \n",
       "\n",
       "  Victim_Descent  Premise_Code     Premise_Description  Weapon_Used_Code  \\\n",
       "0              W         101.0                  STREET               NaN   \n",
       "1              H         216.0               SWAP MEET             400.0   \n",
       "2              B         501.0  SINGLE FAMILY DWELLING               NaN   \n",
       "3              H         101.0                  STREET               NaN   \n",
       "4              X         401.0               MINI-MART             400.0   \n",
       "\n",
       "                               Weapon_Description Status Status_Description  \\\n",
       "0                                             NaN     IC        Invest Cont   \n",
       "1  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     IC        Invest Cont   \n",
       "2                                             NaN     IC        Invest Cont   \n",
       "3                                             NaN     IC        Invest Cont   \n",
       "4  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     IC        Invest Cont   \n",
       "\n",
       "    Crime_Category  \n",
       "0  Property Crimes  \n",
       "1  Property Crimes  \n",
       "2  Property Crimes  \n",
       "3  Property Crimes  \n",
       "4  Property Crimes  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = train_df\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns Information\n",
    "1. **Location:** Location of the incident.\n",
    "2. **Cross_Street:** Cross street near the incident.\n",
    "3. **Latitude:** Latitude coordinate.\n",
    "4. **Longitude:** Longitude coordinate.\n",
    "5. **Date_Reported:** Date when the crime was reported.\n",
    "6. **Date_Occurred:** Date when the crime occurred.\n",
    "7. **Time_Occurred:** Time when the crime occurred.\n",
    "8. **Area_ID:** ID of the area where the crime occurred.\n",
    "9. **Area_Name:** Name of the area where the crime occurred.\n",
    "10. **Reporting_District_no:** Reporting district number.\n",
    "11. **Part 1-2:** Part classification of the crime (1 or 2).\n",
    "12. **Modus_Operandi:** Method of operation used in the crime.\n",
    "13. **Victim_Age:** Age of the victim.\n",
    "14. **Victim_Sex:** Sex of the victim.\n",
    "15. **Victim_Descent:** Descent of the victim.\n",
    "16. **Premise_Code:** Code representing the type of premise where the crime occurred.\n",
    "17. **Premise_Description:** Description of the premise.\n",
    "18. **Weapon_Used_Code:** Code of the weapon used.\n",
    "19. **Weapon_Description:** Description of the weapon used.\n",
    "20. **Status:** Status of the investigation.\n",
    "21. **Status_Description:** Description of the status.\n",
    "22. **Crime_Category:** Category of the crime (target variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Victim_Age\n",
       "0.0     4828\n",
       "30.0     448\n",
       "31.0     446\n",
       "26.0     442\n",
       "29.0     425\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Victim_Age'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                  0.000\n",
       "Cross_Street             82.760\n",
       "Latitude                  0.000\n",
       "Longitude                 0.000\n",
       "Date_Reported             0.000\n",
       "Date_Occurred             0.000\n",
       "Time_Occurred             0.000\n",
       "Area_ID                   0.000\n",
       "Reporting_District_no     0.000\n",
       "Part 1-2                  0.000\n",
       "Modus_Operandi           13.705\n",
       "Victim_Age                0.000\n",
       "Victim_Sex               13.120\n",
       "Victim_Descent           13.120\n",
       "Premise_Code              0.000\n",
       "Weapon_Used_Code         63.325\n",
       "Status                    0.000\n",
       "Crime_Category            0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Area_Name','Premise_Description','Weapon_Description','Status_Description'],axis=1, inplace=True) # Droping columns giving info about IDs\n",
    "data.isna().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Is_Cross_Street'] = data['Cross_Street'].apply(lambda x: 0 if pd.isna(x) else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = data.groupby(['Crime_Category', 'Is_Cross_Street']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=False, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Is_Cross_Street and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Is Cross Street', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         4500    CARPENTER                    AV\n",
       "1                 45TH                         ST\n",
       "2          600 E  MARTIN LUTHER KING JR        BL\n",
       "3        14900    ORO GRANDE                   ST\n",
       "4         7100 S  VERMONT                      AV\n",
       "                           ...                   \n",
       "19995     5100 W  ADAMS                        BL\n",
       "19996    16900    ROSCOE                       BL\n",
       "19997     1000 S  SHENANDOAH                   ST\n",
       "19998      300 W  SEPULVEDA                    ST\n",
       "19999             DALTON                       AV\n",
       "Name: Location, Length: 20000, dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Cross_Street','Is_Cross_Street'], axis=1, inplace=True) # Dropping columns with more null values\n",
    "data['Location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class LocationTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, location_col='Location'):\n",
    "        self.location_col = location_col\n",
    "\n",
    "    def extract_location_data(self, s):\n",
    "        street_number = direction = street_name = street_type = None\n",
    "        try:  # street number\n",
    "            nt = s.split(' ')[0]\n",
    "            street_number = nt if int(nt.isdigit()) else None\n",
    "        except: pass\n",
    "        try:  # direction\n",
    "            nt = [x.strip() for x in s.split(' ') if x.strip()]\n",
    "            for x in nt:\n",
    "                if x.isdigit(): continue\n",
    "                elif len(x) > 1: break\n",
    "                if x in ['N', 'S', 'E', 'W']:\n",
    "                    direction = x\n",
    "                    break\n",
    "        except: pass\n",
    "        try:  # street type\n",
    "            nt = s.split('   ')[-1].strip()\n",
    "            street_type = nt if len(nt) == 2 and not(nt.isdigit()) else None\n",
    "        except: pass\n",
    "        street_name = None  # street name\n",
    "        nt = [x.strip() for x in s.split('  ') if x.strip()]\n",
    "        for x in nt:\n",
    "            test_x = [x.strip() for x in x.split(' ') if x.strip()]\n",
    "            if len(test_x) == 1 and not(street_number and street_number == test_x[0]) and not(street_type and street_type == test_x[0]):\n",
    "                street_name = x\n",
    "                break\n",
    "            elif not(street_number and street_number in test_x):\n",
    "                street_name = x\n",
    "                break\n",
    "        \n",
    "        return street_number, direction, street_name, street_type\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        parsed_data = []\n",
    "        for idx, row in X.iterrows():\n",
    "            string = row[self.location_col]\n",
    "            street_number, direction, street_name, street_type = self.extract_location_data(string)\n",
    "            parsed_data.append({\n",
    "                'Street_Number': street_number,\n",
    "                'Direction': direction,\n",
    "                'Street_Name': street_name,\n",
    "                'Street_Type': street_type\n",
    "            })\n",
    "        location_df = pd.DataFrame(parsed_data, index=X.index)\n",
    "        return pd.concat([location_df, X.drop('Location',axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Street_Number</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Street_Name</th>\n",
       "      <th>Street_Type</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Modus_Operandi</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Crime_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500</td>\n",
       "      <td>None</td>\n",
       "      <td>CARPENTER</td>\n",
       "      <td>AV</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>03/09/2020 12:00:00 AM</td>\n",
       "      <td>03/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0385</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>45TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0906 0352 0371 0446 1822 0344 0416 0417</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>E</td>\n",
       "      <td>MARTIN LUTHER KING JR</td>\n",
       "      <td>BL</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>605.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0329 1202</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900</td>\n",
       "      <td>None</td>\n",
       "      <td>ORO GRANDE</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>11/08/2020 12:00:00 AM</td>\n",
       "      <td>11/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0329 1300</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100</td>\n",
       "      <td>S</td>\n",
       "      <td>VERMONT</td>\n",
       "      <td>AV</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0416 0945 1822 0400 0417 0344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Street_Number Direction            Street_Name Street_Type  Latitude  \\\n",
       "0          4500      None              CARPENTER          AV   34.1522   \n",
       "1          None      None                   45TH          ST   34.0028   \n",
       "2           600         E  MARTIN LUTHER KING JR          BL   34.0111   \n",
       "3         14900      None             ORO GRANDE          ST   34.2953   \n",
       "4          7100         S                VERMONT          AV   33.9787   \n",
       "\n",
       "   Longitude           Date_Reported           Date_Occurred  Time_Occurred  \\\n",
       "0  -118.3910  03/09/2020 12:00:00 AM  03/06/2020 12:00:00 AM         1800.0   \n",
       "1  -118.2391  02/27/2020 12:00:00 AM  02/27/2020 12:00:00 AM         1345.0   \n",
       "2  -118.2653  08/21/2020 12:00:00 AM  08/21/2020 12:00:00 AM          605.0   \n",
       "3  -118.4590  11/08/2020 12:00:00 AM  11/06/2020 12:00:00 AM         1800.0   \n",
       "4  -118.2918  02/25/2020 12:00:00 AM  02/25/2020 12:00:00 AM         1130.0   \n",
       "\n",
       "   Area_ID  Reporting_District_no  Part 1-2  \\\n",
       "0     15.0                 1563.0       1.0   \n",
       "1     13.0                 1367.0       1.0   \n",
       "2     13.0                 1343.0       2.0   \n",
       "3     19.0                 1924.0       1.0   \n",
       "4     12.0                 1245.0       1.0   \n",
       "\n",
       "                            Modus_Operandi  Victim_Age Victim_Sex  \\\n",
       "0                                     0385        75.0          M   \n",
       "1  0906 0352 0371 0446 1822 0344 0416 0417        41.0          M   \n",
       "2                                0329 1202        67.0          M   \n",
       "3                                0329 1300        61.0          M   \n",
       "4            0416 0945 1822 0400 0417 0344         0.0          X   \n",
       "\n",
       "  Victim_Descent  Premise_Code  Weapon_Used_Code Status   Crime_Category  \n",
       "0              W         101.0               NaN     IC  Property Crimes  \n",
       "1              H         216.0             400.0     IC  Property Crimes  \n",
       "2              B         501.0               NaN     IC  Property Crimes  \n",
       "3              H         101.0               NaN     IC  Property Crimes  \n",
       "4              X         401.0             400.0     IC  Property Crimes  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = LocationTransformer(location_col='Location')\n",
    "data = transformer.fit_transform(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Number    17.240\n",
       "Direction        64.765\n",
       "Street_Name       0.000\n",
       "Street_Type       9.035\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Street_Number','Direction','Street_Name','Street_Type']].isna().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count plot for Crime_Category and Street_Type\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(data=data, x='Street_Type', hue='Crime_Category')\n",
    "plt.title('Crime Category by Street Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_date_info():\n",
    "    data['Date_Reported'] = pd.to_datetime(data['Date_Reported'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "    data['Date_Occurred'] = pd.to_datetime(data['Date_Occurred'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "    def convert_time_occurred(time):\n",
    "        time_str = f\"{int(time):04d}\"\n",
    "        return f\"{time_str[:2]}:{time_str[2:]}:00\"\n",
    "    data['Time_Occurred'] = data['Time_Occurred'].apply(convert_time_occurred)\n",
    "\n",
    "    data['Days_After_Reported'] = (data['Date_Reported'] - data['Date_Occurred']).dt.days\n",
    "\n",
    "    data['Reported_Day'] = data['Date_Reported'].dt.day\n",
    "    data['Reported_Month'] = data['Date_Reported'].dt.month\n",
    "    data['Reported_Weekday'] = data['Date_Reported'].dt.weekday\n",
    "    data['Occurred_Day'] = data['Date_Occurred'].dt.day\n",
    "    data['Occurred_Month'] = data['Date_Occurred'].dt.month\n",
    "    data['Occurred_Weekday'] = data['Date_Occurred'].dt.weekday\n",
    "    data['Occurred_Weekmonth'] = data['Date_Occurred'].apply(lambda d: (d.day - 1) // 7 + 1)\n",
    "    data['Occurred_Weekend?'] = np.where(data['Occurred_Weekday'].isin([5, 6]), 1, 0)\n",
    "    data['Occurred_Hour'] = pd.to_datetime(data['Time_Occurred'], format='%H:%M:%S').dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Street_Number</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Street_Name</th>\n",
       "      <th>Street_Type</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Modus_Operandi</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Crime_Category</th>\n",
       "      <th>Days_After_Reported</th>\n",
       "      <th>Reported_Day</th>\n",
       "      <th>Reported_Month</th>\n",
       "      <th>Reported_Weekday</th>\n",
       "      <th>Occurred_Day</th>\n",
       "      <th>Occurred_Month</th>\n",
       "      <th>Occurred_Weekday</th>\n",
       "      <th>Occurred_Weekmonth</th>\n",
       "      <th>Occurred_Weekend?</th>\n",
       "      <th>Occurred_Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500</td>\n",
       "      <td>None</td>\n",
       "      <td>CARPENTER</td>\n",
       "      <td>AV</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0385</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>45TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>13:45:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0906 0352 0371 0446 1822 0344 0416 0417</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>E</td>\n",
       "      <td>MARTIN LUTHER KING JR</td>\n",
       "      <td>BL</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>06:05:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0329 1202</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900</td>\n",
       "      <td>None</td>\n",
       "      <td>ORO GRANDE</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0329 1300</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100</td>\n",
       "      <td>S</td>\n",
       "      <td>VERMONT</td>\n",
       "      <td>AV</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0416 0945 1822 0400 0417 0344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Street_Number Direction            Street_Name Street_Type  Latitude  \\\n",
       "0          4500      None              CARPENTER          AV   34.1522   \n",
       "1          None      None                   45TH          ST   34.0028   \n",
       "2           600         E  MARTIN LUTHER KING JR          BL   34.0111   \n",
       "3         14900      None             ORO GRANDE          ST   34.2953   \n",
       "4          7100         S                VERMONT          AV   33.9787   \n",
       "\n",
       "   Longitude Date_Reported Date_Occurred Time_Occurred  Area_ID  \\\n",
       "0  -118.3910    2020-03-09    2020-03-06      18:00:00     15.0   \n",
       "1  -118.2391    2020-02-27    2020-02-27      13:45:00     13.0   \n",
       "2  -118.2653    2020-08-21    2020-08-21      06:05:00     13.0   \n",
       "3  -118.4590    2020-11-08    2020-11-06      18:00:00     19.0   \n",
       "4  -118.2918    2020-02-25    2020-02-25      11:30:00     12.0   \n",
       "\n",
       "   Reporting_District_no  Part 1-2                           Modus_Operandi  \\\n",
       "0                 1563.0       1.0                                     0385   \n",
       "1                 1367.0       1.0  0906 0352 0371 0446 1822 0344 0416 0417   \n",
       "2                 1343.0       2.0                                0329 1202   \n",
       "3                 1924.0       1.0                                0329 1300   \n",
       "4                 1245.0       1.0            0416 0945 1822 0400 0417 0344   \n",
       "\n",
       "   Victim_Age Victim_Sex Victim_Descent  Premise_Code  Weapon_Used_Code  \\\n",
       "0        75.0          M              W         101.0               NaN   \n",
       "1        41.0          M              H         216.0             400.0   \n",
       "2        67.0          M              B         501.0               NaN   \n",
       "3        61.0          M              H         101.0               NaN   \n",
       "4         0.0          X              X         401.0             400.0   \n",
       "\n",
       "  Status   Crime_Category  Days_After_Reported  Reported_Day  Reported_Month  \\\n",
       "0     IC  Property Crimes                    3             9               3   \n",
       "1     IC  Property Crimes                    0            27               2   \n",
       "2     IC  Property Crimes                    0            21               8   \n",
       "3     IC  Property Crimes                    2             8              11   \n",
       "4     IC  Property Crimes                    0            25               2   \n",
       "\n",
       "   Reported_Weekday  Occurred_Day  Occurred_Month  Occurred_Weekday  \\\n",
       "0                 0             6               3                 4   \n",
       "1                 3            27               2                 3   \n",
       "2                 4            21               8                 4   \n",
       "3                 6             6              11                 4   \n",
       "4                 1            25               2                 1   \n",
       "\n",
       "   Occurred_Weekmonth  Occurred_Weekend?  Occurred_Hour  \n",
       "0                   1                  0             18  \n",
       "1                   4                  0             13  \n",
       "2                   3                  0              6  \n",
       "3                   1                  0             18  \n",
       "4                   4                  0             11  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_date_info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mTdayyMjIRO89cuRIY7FYzD///GMt6969u7nfT1ySGTp0qPXvJk2aGDc3N3PixAlr2fnz502GDBlMjRo1En3GunXrmri4OGt57969jbOzs7l+/foDv6N27doZSebtt9+2+dwNGzY0bm5u5vLly8YYY3755RcjySxYsMDm9atXr05UXqJECRMUFJTovbp37278/f2tf/fp08fUqFHD+Pn5malTpxpjjLl69aqxWCxm4sSJ1lgKFSpk6tWrZ/P5IiMjTUBAgHnuueesZR07djTZs2c3V65csXnfli1bGh8fH+t+it+HxYoVM7dv37auN3HiRCPJHDx48L7f1/Xr140k07hx4/uuY4wxL730kpFkwsPDjTHGtG3b1jg5OZk9e/YkWjf+cw0ZMsRIMsuWLbvvOvH7+9SpUzbLk6qXQUFBRpKZNm2azbqnTp0ykoy3t7cJCQmxWfawv4NevXoZSWbXrl3WspCQEOPj45NkfA9y6NAhI8l89NFHxhhj7ty5Y7y8vMy8efOMMcb4+/ubyZMnG2OMCQ8PN87OzqZz587GGGNOnz5tnJ2dzccff2yzzYMHDxoXFxdreXLq0dChQ40kc/nyZXP48GGTI0cOU758eRMaGmpd56OPPjJeXl7m2LFjNu87YMAA4+zsbM6cOWOM+d93nSVLFpvXr1ixwkgyq1atspaVKVPGZM+e3eY3u3btWiPJ5M2bN8nv7s8//zSZM2c25cqVMxEREQ/6mq2f91716tUz+fPntynLmzevkWS2bt1qLQsJCTHu7u7m3XfftZY9Sj34448/jCTTs2fPf43bmAfX2/hlc+bMsZbFH9s++eQTa9m1a9eMp6ensVgsZtGiRdbyI0eOJDoGP+w+/jfx+3r8+PEPtX5MTIzNcSk+bn9/f/PGG29Yyy5fvpwo5ngP+zt+++23jcViMfv377eWXb161WTOnNlm/4WEhBg3Nzfz/PPPm9jYWOu6kyZNMpLM7NmzrWX3O+7ExsaaXLlymRYtWtiUjxs3zlgsFnPy5MkHfi/VqlUzgYGBicofdOybP3++tez27dsmW7ZsplmzZg98H2Pu1v/nn3/eXL582Vy+fNn88ccfpmXLljbnyuSeb/Lnz2/z+4uJiTEBAQEmb9685tq1azbbSHiMeth9mZxrgfudo6Oiomz2rzF3f1vu7u5m+PDh1rKxY8caSeb777+3lt26dcsULVrUZl8k57gbr0uXLsbT0zNROWAPPF4OpLK6devK19dXuXPn1iuvvCIvLy+tXLnS2oIUGhqqjRs3qnnz5rpx44auXLmiK1eu6OrVq6pXr56OHz/+wNHOE7bq3Lx5U1euXFGVKlVkjNH+/fuTHW9sbKzWrl2rJk2a2PQJzp49u1577TVt27bN+mhavC5dutg8rl69enXFxsbqn3/+eaj37NGjh/X/4+/cR0dHa/369ZLutub7+Pjoueees34/V65cUWBgoNKnT5/kHe57Va9eXZcuXdLRo0cl3W3RrlGjhqpXr65ffvlF0t3Wb2OMtRX5999/1/Hjx/Xaa6/p6tWr1ve9efOm6tSpo61btyouLk7GGC1dulQvvviijDE2MdarV09hYWHat2+fTTwdOnSw6cMc/54nT56872e4ceOGJP1ri1X88vDwcMXFxen777/Xiy++aB1bIKH4/bZ06VKVLl06yacqEu7b5HB3d1eHDh2SXNasWTObx+OT8zv46aefVKlSJZtWCl9fX7Vu3TrZMRYrVkxZsmSx9tX+448/dPPmTevo5FWqVLG2fOzYsUOxsbHW/tzLli1TXFycmjdvbrPPs2XLpkKFClnr5cPWo4T+/PNPBQUFKV++fFq/fr3NkzGLFy9W9erVlSlTJpv3rVu3rmJjY7V161abbbVo0cLm9ffWtQsXLuj3339Xu3bt5OPjY13vueeeU/HixZP83m7fvq3GjRsrY8aM+vnnn+Xl5fWv33XCY1VYWJiuXLmioKAgnTx5UmFhYTbrFi9e3OZpDl9fXxUpUsTm9/Eo9SD+GPYwrb8J3Vtv/02nTp2s/58xY0YVKVJEXl5eNuN5xD+yn/CzJXcf309yP6ezs7P1uBQXF6fQ0FDFxMSoXLlyiY5hSUnO73j16tWqXLmyypQpY3195syZE+2/9evXKzo6Wr169bLph9y5c2d5e3vbPNUjJX3ccXJyUuvWrbVy5UrrcVSSFixYoCpVqlifMLifq1ev2vyG/k369Olt+mS7ubmpQoUKDzy+J7R27Vr5+vrK19dXpUuX1uLFi9WmTRuNGjUqReebdu3a2fz+9u/fr1OnTqlXr16JuovEH+9Tcm3yKNcC7u7u1v0bGxurq1evKn369CpSpIjN51m9erVy5sypl156yVrm4eFhM5WblLLjbqZMmXTr1i1FRkb+a7zAo+LxciCVTZ48WYULF1ZYWJhmz56trVu32jw+9/fff8sYo8GDB2vw4MFJbiMkJEQ5c+ZMctmZM2c0ZMgQrVy50qZ/s6REF7IP4/Lly4qMjEyyj1uxYsUUFxen4OBg6yOekhI9nhl/cXJvPElxcnJKNOBX4cKFJf3vUfzjx48rLCxMfn5+SW4jJCTkX98n/gL+l19+Ua5cubR//36NGDFCvr6++uyzz6zLvL29Vbp0aev7SncvWO4nLCxMd+7c0fXr1zVjxgzNmDHjoWJMyXcWf+Gc8KIxKQmT88uXLys8PFwlS5Z84GtOnDihZs2aPXCd5MqZM+d9B0e79yI3Ob+Df/75J1EXB0n/2i8zKRaLRVWqVLFegG3fvl1+fn7WEYqrVKmiSZMmSZI1+Y5Puo8fPy5jjAoVKpTktuMfeX3YepTwov7FF1+Uv7+/1qxZk2gu++PHj+vAgQP3Tf6SW9fiL4iT+hz3XvDG27Fjh06cOKGFCxc+dNeD7du3a+jQodqxY0eii9qwsDCbhD+pR74zZcpk8/t4lHrg7e0t6d9/S/f6t+QsIQ8Pj0T7yMfHR7ly5Up0I8vHx8fmsyV3H99PSj7nvHnzNHbsWB05csSmD/TDfPbk/o4rV66caHnC0cGl/9XPe/erm5ub8ufPnyihu99xp23btho1apSWL1+utm3b6ujRo9q7d+9DjwhukjGqdVL7OFOmTDpw4MBDvb5ixYoaMWKELBaL0qVLp2LFilmT45CQkGSfb+7dd/FjyjzovJCSa5NHuRaIi4vTxIkTNWXKFJ06dcpmur74x+qlu/WhQIECib7fe+tNSo678fs4pTeageQg6QZSWYUKFawtjE2aNFG1atX02muv6ejRo0qfPr31Tmvfvn1Vr169JLdx78kkXmxsrJ577jmFhobqvffeU9GiReXl5aVz586pffv2KRpYKiXuN2Jxci5SHiQuLk5+fn5asGBBkssfpuUpfqTqrVu3Kl++fDLGqHLlyvL19VXPnj31zz//6JdfflGVKlWsd9vjv78xY8bYtMYklD59el29elWS9Prrr9/3BB/f3zFeSr4zHx8fZc+e/V8v3A4cOKCcOXPK29vbZoCwR3W/C5H7zWX8oL619y57lN/Bo6pWrZpWrVqlgwcPWvtzx6tSpYr69eunc+fOadu2bcqRI4f1JlFcXJwsFot+/vnnJPdnfLL8sPUooWbNmmnevHlasGCBunbtarMsLi5Ozz33nPr375/ktuJvWsWzx+8zvs5nz579odY/ceKE6tSpo6JFi2rcuHHKnTu33Nzc9NNPP2n8+PGJjlX2PqYULFhQLi4uOnjwYLJel5xxA+73GR7msyV3H99P0aJFJemhP+fXX3+t9u3bq0mTJurXr5/8/Pzk7OyskSNHJhr8MymO/B3Hu98+Kl68uAIDA/X111+rbdu2+vrrr+Xm5vZQs4hkyZLloRLHeI9af7NmzZpo0LZ48d9xcs43KRnvIiX78lE+9yeffKLBgwfrjTfe0EcffaTMmTPLyclJvXr1StG1TEqOu9euXVO6dOlS9H0ByUXSDdhR/MVLrVq1NGnSJA0YMMB6Ae/q6nrfk+z9HDx4UMeOHdO8efPUtm1ba/m6desSrfuwd259fX2VLl0662PYCR05ckROTk7KnTt3suJ8kLi4OJ08edLmIvLYsWOS7o50KkkFChTQ+vXrVbVq1X89GT7oc1avXl1bt25VQECAypQpowwZMqh06dLy8fHR6tWrtW/fPpu5zAsUKCDpbmvRg/aNr6+vMmTIoNjY2GTvw+Rq1KiRvvzyS23bti3Jaat++eUXnT592pqo+fr6ytvb+4FTH0l3P+u/rRPfInDviMEP243gQZLzO8ibN6+1FSOhpOrsw0g4X/f27dttBiEKDAyUu7u7Nm/ebB3pN16BAgVkjFFAQMADk6CHrUcJjRkzRi4uLnrrrbeUIUMGvfbaazbbi4iISLW6Fj84VHK+0wIFCqh79+73fQLnXqtWrdLt27e1cuVKm9awh+kacj+PUg/SpUun2rVra+PGjQoODk7VY1pqSK19XLhwYRUpUkQrVqzQxIkTEyUZ91qyZIny58+vZcuW2RxL7x1o7H7H2eT+jpOaseHesvj6efToUZunoqKjo3Xq1KlkfUdt27ZVnz59dOHCBS1cuFANGzZ8qMfGixYtqqVLlz70+9hTapxv4o9Jf/7553238SjXJg9yv7qzZMkS1apVS7NmzbIpv379us3TNHnz5tVff/0lY4zNtu6tNyk57p46dUrFihV7qHWBR0WfbsDOatasqQoVKmjChAmKioqSn5+fatasqenTp+vChQuJ1n/Q1Fvxd5UT3kU2xiSa8kOStc/lvQlTUtt8/vnntWLFCpspWy5duqSFCxeqWrVq1kcWU0v847vS3fgnTZokV1dX1alTR9Ld+TNjY2P10UcfJXptTEyMzWfy8vK672esXr26Tp8+rW+//db6uLmTk5OqVKmicePG6c6dOzb9SAMDA1WgQAF99tlnioiISLS9+H3j7OysZs2aaenSpUkmrsmZPu3f9OvXT56enuratau1tTFeaGio3nzzTaVLl079+vWzfr4mTZpo1apV+u233xJtL77uNGvWTH/88UeS00TFrxN/EZOwP2lsbOx9H3FMjuT8Dho0aKCdO3dq9+7dNsvv9yTEvylXrpw8PDy0YMECnTt3zqal293dXWXLltXkyZN18+ZNmxsdTZs2lbOzs4YNG5aoJccYY90/D1uPErJYLJoxY4ZeeeUVtWvXzmaKnubNm2vHjh1as2ZNotddv379X6fuulf27NlVpkwZzZs3z6ZLyrp166xTud0rICBAPXr0eOikO6ljVVhYmObMmZOsWBN61HowdOhQGWPUpk2bJPfL3r17NW/evBTH9yhScx8PGzZMV69eVadOnZJ83dq1a/XDDz9ISno/7dq1Szt27LB5Tfy81/cea5PzO65Xr5527Nih33//3VoWGhqaaP/VrVtXbm5u+vzzz23imjVrlsLCwtSwYcMHfXwbrVq1ksViUc+ePXXy5EmbftcPUrlyZV27du2h+2TbU2qcb8qWLauAgABNmDAh0T6M/44f5drkQe53jnZ2dk50HF28eHGifuP16tXTuXPnbI6JUVFR+vLLL23WS8lxd9++fTbHf8CeaOkGHoN+/frp1Vdf1dy5c/Xmm29q8uTJqlatmkqVKqXOnTsrf/78unTpknbs2KGzZ88mOU+ldPfue4ECBdS3b1+dO3dO3t7eWrp0aZKPwQUGBkqS3nnnHdWrV0/Ozs5q2bJlktsdMWKE1q1bp2rVqumtt96Si4uLpk+frtu3bz/SXKNJ8fDw0OrVq9WuXTtVrFhRP//8s3788UcNGjTI+th4UFCQunbtqpEjR+r333/X888/L1dXVx0/flyLFy/WxIkT9corr1g/59SpUzVixAgVLFhQfn5+ql27tqT/9es+evSoPvnkE2sMNWrU0M8//2ydwziek5OTZs6cqfr166tEiRLq0KGDcubMqXPnzmnTpk3y9vbWqlWrJN2dAmzTpk2qWLGiOnfurOLFiys0NFT79u3T+vXrFRoamirfV6FChTRv3jy1bt1apUqVUseOHRUQEKDTp09r1qxZunLlir755htrgizdfWxv7dq1CgoKUpcuXVSsWDFduHBBixcv1rZt25QxY0b169dPS5Ys0auvvqo33nhDgYGBCg0N1cqVKzVt2jSVLl1aJUqUUKVKlTRw4ECFhoYqc+bMWrRoUbKTvPt52N9B//799dVXX+mFF15Qz549rVNF5c2b96H7TCbk5uam8uXL65dffpG7u7v1txKvSpUqGjt2rCTZJN0FChTQiBEjNHDgQJ0+fVpNmjRRhgwZdOrUKS1fvlxdunRR3759k1WPEnJyctLXX3+tJk2aqHnz5vrpp59Uu3Zt9evXTytXrlSjRo2s02jdvHlTBw8e1JIlS3T69OlkT/E2cuRINWzYUNWqVdMbb7yh0NBQffHFFypRokSSF6zLly9Xhw4dtGnTJutUaw/y/PPPy83NTS+++KK6du2qiIgIffnll/Lz80vygv5hPGo9qFKliiZPnqy33npLRYsWVZs2bVSoUCHduHFDmzdv1sqVKzVixIgUxfaoUnMft2jRQgcPHtTHH3+s/fv3q1WrVsqbN6+uXr2q1atXa8OGDdZ5whs1aqRly5bp5ZdfVsOGDXXq1ClNmzZNxYsXt6kHnp6eKl68uL799lsVLlxYmTNnVsmSJVWyZMlk/Y6//vprPffcc3r77betU4blyZNHoaGh1lZMX19fDRw4UMOGDdMLL7ygl156SUePHtWUKVNUvnz5h06c47f1wgsvaPHixcqYMeNDJ+wNGzaUi4uL1q9fry5dujz0+9nLo55vnJycNHXqVL344osqU6aMOnTooOzZs+vIkSM6dOiQ9WZPSq9NHuR+5+hGjRpp+PDh6tChg6pUqaKDBw9qwYIFicZ86dq1qyZNmqRWrVqpZ8+eyp49uxYsWCAPDw9J/2tJT+5xd+/evQoNDVXjxo2T/ZmAFLHv4OjAf0f8FBpJTdMUGxtrChQoYAoUKGBiYmKMMcacOHHCtG3b1mTLls24urqanDlzmkaNGpklS5ZYX5fU9CR//fWXqVu3rkmfPr3JmjWr6dy5s3U6nIRT2MTExJi3337b+Pr6GovFYjN9mJKY+mXfvn2mXr16Jn369CZdunSmVq1a5tdff32oz5hUnElp166d8fLyMidOnDDPP/+8SZcunfH39zdDhw5NNHWIMcbMmDHDBAYGGk9PT5MhQwZTqlQp079/f3P+/HnrOhcvXjQNGzY0GTJkMJISTU3i5+dnJJlLly5Zy7Zt22YkmerVqycZ5/79+03Tpk1NlixZjLu7u8mbN69p3ry52bBhg816ly5dMt27dze5c+c2rq6uJlu2bKZOnTpmxowZib6bxYsX27w2qWmHHuTAgQOmVatWJnv27Nb3atWq1X2nHPvnn39M27ZtrVPW5c+f33Tv3t1meqCrV6+aHj16mJw5cxo3NzeTK1cu065dO5tpaU6cOGHq1q1r3N3djb+/vxk0aJBZt25dktPmlChRIlEc8Z9zzJgxScb5ML+D+M8fFBRkPDw8TM6cOc1HH31kZs2alewpw+INHDjQSDJVqlRJtGzZsmVGksmQIYP195rQ0qVLTbVq1YyXl5fx8vIyRYsWNd27dzdHjx61We9h6lHCKcPiRUZGmqCgIJM+fXqzc+dOY4wxN27cMAMHDjQFCxY0bm5uJmvWrKZKlSrms88+M9HR0caYB3/XSf3mly5daooVK2bc3d1N8eLFzbJly0y7du2SnDIs/rf/b7/xhFauXGmeeeYZ4+HhYfLly2dGjRplZs+enWif5c2b1zRs2DDR64OCghL9nlOjHuzdu9e89tprJkeOHMbV1dVkypTJ1KlTx8ybN896HHrQd3m/KcO8vLyS/AxJ/S6S+swPs4+TY8OGDaZx48bGz8/PuLi4GF9fX/Piiy+aFStWWNeJi4szn3zyicmbN69xd3c3zz77rPnhhx+SrAe//vqrCQwMNG5ubonq08P+jvfv32+qV69u3N3dTa5cuczIkSPN559/biSZixcv2qw7adIkU7RoUePq6mr8/f1Nt27dEk13db/vN6HvvvvOSDJdunR5+C/P3J2KsU6dOjZl95syLKkY7vdbutf96v+9HuV8E2/btm3mueeeMxkyZDBeXl7mmWeeMV988YXNOg+zL5NzLXC/c3RUVJR59913Tfbs2Y2np6epWrWq2bFjR5K/+5MnT5qGDRsaT09P4+vra959912zdOlSI8l6jIz3sOfv9957z+TJk8dmejHAnizGpNIoJQAAAEAy9OrVS9OnT1dERMR9B+Z6FCtWrFCTJk20detWm+5E/+aXX35RzZo1deTIkfvOWADHmTBhgnr37q2zZ88+dLeXeLdv31a+fPk0YMAA9ezZ004RArZIugEAAGB3t27dshkc8+rVqypcuLDKli2b5ICgqaFRo0Y6fPiw/v7772RPDVW/fn3lypUrUf9hPF731puoqCg9++yzio2NtQ7EmhzTpk3TJ598ouPHj9tM6QrYE0k3ACDNi4iISLIvckK+vr52aUkDHofQ0FBFR0ffd7mzs/NDTafoSGXKlFHNmjVVrFgxXbp0SbNmzdL58+e1YcMG1ahRI1Xfa9GiRTpw4IBGjhypiRMn6p133knV7ePxqV+/vvLkyaMyZcooLCxMX3/9tQ4dOqQFCxbYzPQAPMlIugEAad6HH35oM/1bUk6dOmWdlg5Ia2rWrKktW7bcd3nevHltZqB4Eg0aNEhLlizR2bNnZbFYVLZsWQ0dOtQuUy9aLBalT59eLVq00LRp0+TiwtjBadWECRM0c+ZMnT59WrGxsSpevLj69++vFi1aODo04KGRdAMA0ryTJ0/+6/Q+1apVs454C6Q1e/fuTXKminienp6qWrXqY4wIAPCwSLoBAAAAALATJ0cHAAAAAADA04oOLpLi4uJ0/vx5ZciQIdkjWwIAAAAA/nuMMbpx44Zy5MghJ6f7t2eTdEs6f/68cufO7egwAAAAAABpTHBwsHLlynXf5STdkjJkyCDp7pfl7e3t4GgAAAAAAE+68PBw5c6d25pP3g9Jt2R9pNzb25ukGwAAAADw0P6tizIDqQEAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkKfbgAAAAB4isXGxurOnTuODiPNcXV1lbOz8yNvh6QbAAAAAJ5CxhhdvHhR169fd3QoaVbGjBmVLVu2fx0s7UFIugEAAADgKRSfcPv5+SldunSPlDj+1xhjFBkZqZCQEElS9uzZU7wtkm4AAAAAeMrExsZaE+4sWbI4Opw0ydPTU5IUEhIiPz+/FD9qzkBqAAAAAPCUie/DnS5dOgdHkrbFf3+P0ieepBsAAAAAnlI8Uv5oUuP7I+kGAAAAAMBOHJp0b926VS+++KJy5Mghi8Wi77//3ma5MUZDhgxR9uzZ5enpqbp16+r48eM264SGhqp169by9vZWxowZ1bFjR0VERDzGTwEAAAAA+Dc1a9ZUr169Hvv75suXTxMmTHjs7xvPoUn3zZs3Vbp0aU2ePDnJ5aNHj9bnn3+uadOmadeuXfLy8lK9evUUFRVlXad169Y6dOiQ1q1bpx9++EFbt25Vly5dHtdHAAAAAIA0Zdq0acqQIYNiYmKsZREREXJ1dVXNmjVt1t28ebMsFotOnDjxmKO0j6tXr6p79+7KmzevvLy8VKVKFe3bt8+u7+nQpLt+/foaMWKEXn755UTLjDGaMGGCPvjgAzVu3FjPPPOM5s+fr/Pnz1tbxA8fPqzVq1dr5syZqlixoqpVq6YvvvhCixYt0vnz5x/zpwEAAACAJ1+tWrUUERGh3377zVr2yy+/KFu2bNq1a5dNI+emTZuUJ08eFShQwBGhprpjx47JyclJ3333nfbt2yc/Pz81a9bMru/5xPbpPnXqlC5evKi6detay3x8fFSxYkXt2LFDkrRjxw5lzJhR5cqVs65Tt25dOTk5adeuXffd9u3btxUeHm7zDwAAAAD+C4oUKaLs2bNr8+bN1rLNmzercePGCggI0M6dO23Ka9Wqpbi4OI0cOVIBAQHy9PRU6dKltWTJEpvt/vnnn6pfv77Sp08vf39/tWnTRleuXLlvHD/++KN8fHy0YMECSVJwcLCaN2+ujBkzKnPmzGrcuLFOnz5tXb99+/Zq0qSJPvvsM2XPnl1ZsmRR9+7dbUYWDwkJ0YsvvihPT08FBARYtx2vcuXK+uKLL1SxYkUVKVJEbdu21YULF2xa/VPbEztP98WLFyVJ/v7+NuX+/v7WZRcvXpSfn5/NchcXF2XOnNm6TlJGjhypYcOGpXLE9hF3sbCjQ0hTnLIdc3QIaQr1K3moX8lHHUse6ljyUL+Sh/qVPNSv5KOOJY+961jcnRwysR/I3Lkj45x4BO5aQc9q08ZVeu/dhpKkTRt/Ur932yvmTkltXL9IQVWz6NatKO3atVMd2j6nT0b01IKFP2rqpPdUqGBebd22V6+/3lpZM91QUI1yun49XLVrv6iOHZpq3OiFunUrSgPen6DmrzbQhrWz7r6puSkTd0XmzkEt/OZHdesxQgvmf6pGDZ9RdOQ+1Xv+VVWqVFpbN86Si4uLPh45Qy/Uq6U/9i2Vu1fZu3Fu2qTs2bNr06ZN+vvvv9WiRQuVKVNGnTt3lnQ3MT9//rw2bdokV1dXvfPOOwoJCUnyO7p+/bqGDx+utm3bysXFfqnxE5t029PAgQPVp08f69/h4eHKnTu3AyMCAAAAgMenZs3y6v3uaMXExOjWrdva//sRBdUopzt3YjT9y8WSpB07/9Dt29GqGVReJUo30brVX6pypdKSpPz5c2nb9n2a8eViBdUop0lTFunZMsX0yYie1veYNWOY8uR/XseOnVbhwvms5ZOnLtIHQ77QyuVfKKjG3aeWv/1ujeLi4jRz+ofWabpmz/xImXyravOWParX4G7SnSlTJk2aNEnOzs4qWrSoGjZsqA0bNqhz5846duyYfv75Z+3evVvly5e/G8OsWSpWrFiizx8eHq5atWopf/789x1jLLU8sUl3tmzZJEmXLl1S9uzZreWXLl1SmTJlrOvce9ciJiZGoaGh1tcnxd3dXe7u7qkfNAAAAACkATVrlNfNm7e057dDunYtXIUL5ZWvb2YF1SinNzoPUVTUbW3e8pvy58+liIhIRUZG6fn6tgNWR0ff0bNlikqSDhw4qk2bdytDpoqJ3uvEyWBr0r102TqFhIRq25b5Kl+upHWdPw4e1d8nguWduZLNa6OibuvEyWDr3yVKlJCzs7P17+zZs+vgwYOS7o755eLiosDAQOvyokWLKmPGjIlimj59ukJDQ7Vr1y65uro+5LeWMk9s0h0QEKBs2bJpw4YN1iQ7PDxcu3btUrdu3STdfR7/+vXr2rt3r/WL3bhxo+Li4lSxYuKdDQAAAACQChbMo1y5/LVp825dux6uGjXu5lM5cvgpd+5s+nXH79q8Zbdq1aygiJuRkqQfVkxWzhy23Xvd3d0kSRE3I/ViwyB9+knvRO+VPXtW6/8/W7qo9v1+RLPnLle5wBLWVu2IiEgFli2mr+d9muj1vr6ZrP9/b4JssVgUFxeX7M9//vx5BQQEyM3NLdmvTS6HJt0RERH6+++/rX+fOnVKv//+uzJnzqw8efKoV69eGjFihAoVKqSAgAANHjxYOXLkUJMmTSRJxYoV0wsvvKDOnTtr2rRpunPnjnr06KGWLVsqR44cDvpUAAAAAPDkqxVUXlu2/qZr18LVt097a3n1aoH6ec027d7zp97s0lzFixWQu7ubzpy5YH0c/F7PlimmZcvXK1++HA/sH52/QG59Nrqvaj3XUc7Ozpo0cZAkqeyzxfTd4jXy88ssb+/0Kfo8RYsWVUxMjPbu3Wt9vPzo0aO6fv16onX79OmjyMjIFL1Pcjl09PLffvtNzz77rJ599llJdz/4s88+qyFDhkiS+vfvr7fffltdunRR+fLlFRERodWrV8vDw8O6jQULFqho0aKqU6eOGjRooGrVqmnGjBkO+TwAAAAAkFbUrFlB27bv1+9/HLVJpoOqB2rGl0sUHX1HtWpWUIYMXnq3dzv16TdG8+av0IkTwdq3/y99MXmh5s1fIUnq3q2lQq+F6bXX39Oe3/7UiRPBWrN2u97oNFixsbE271u4cD5tXDtTy5avV693R0mSWrdqqKxZMqlJs576ZdtenTp1Vpu37NE7vT/V2bP3HyQ7oSJFiuiFF15Q165dtWvXLu3du1edOnWSp6dnonWnTJmijz/+OKVfXbI4tKW7Zs2aMsbcd7nFYtHw4cM1fPjw+66TOXNmLVy40B7hAQAAAMBTq1ZQed26FaWiRQLk75/FWh5Uo5xu3LipIoXzKXt2X0nSR8N6yNc3kz4dPUsnTw1TxowZVPbZYhr4XidJdx9L37Z5vgYMmqB6Dbrq9u07ypsnu+rVqyonp8RtvUWKBGjDmpmq9dwbcnZ21tjRfbVl4xwNGDRezZr30Y0bN5Uzp59q16qYrJbvOXPmqFOnTgoKCpK/v79GjBihwYMHJ1rvwoULOnPmTHK/shSxmAdlvf8R4eHh8vHxUVhYmLy9vR0djg2mq0gepqpIHupX8lC/ko86ljzUseShfiUP9St5qF/JRx1LHnvXsag7OXQ67AMF5PWTh0fiKcPSGotrKYe8b1RUlE6dOqWAgACbJ66lh88jHfp4OQAAAAAATzOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATkm4AAAAAAOzExdEBAAAAAACeTM+7D3+s77f29pAUvW7y5MkaM2aMLl68qNKlS+uLL75QhQoVUjm6lKGlGwAAAACQZn377bfq06ePhg4dqn379ql06dKqV6+eQkJCHB2aJJJuAAAAAEAaNm7cOHXu3FkdOnRQ8eLFNW3aNKVLl06zZ892dGiSSLoBAAAAAGlUdHS09u7dq7p161rLnJycVLduXe3YscOBkf0PSTcAAAAAIE26cuWKYmNj5e/vb1Pu7++vixcvOigqWyTdAAAAAADYCUk3AAAAACBNypo1q5ydnXXp0iWb8kuXLilbtmwOisoWSTcAAAAAIE1yc3NTYGCgNmzYYC2Li4vThg0bVLlyZQdG9j/M0w0AAAAASLP69Omjdu3aqVy5cqpQoYImTJigmzdvqkOHDo4OTRJJNwAAAAAgDWvRooUuX76sIUOG6OLFiypTpoxWr16daHA1RyHpBgAAAAAkae3tIY4O4aH06NFDPXr0cHQYSaJPNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ24ODoAAAAAAMCTyVxt9ljfz5JlabJfs3XrVo0ZM0Z79+7VhQsXtHz5cjVp0iT1g0shWroBAAAAAGnWzZs3Vbp0aU2ePNnRoSSJlm4AAAAAQJpVv3591a9f39Fh3Bct3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCaOXAwAAAADSrIiICP3999/Wv0+dOqXff/9dmTNnVp48eRwY2V0k3QAAAACANOu3335TrVq1rH/36dNHktSuXTvNnTvXQVH9D0k3AAAAACBJlixLHR3Cv6pZs6aMMY4O477o0w0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAABPnThJ5okeYCwtSI3vj6QbAAAAAJ4yrs5hkolRZBRJ96OIjIyUJLm6uqZ4G0wZBgAAAABPGWenW8rosUmXQxpIyqh0HhZZLBZHh5Viltiox/p+xhhFRkYqJCREGTNmlLOzc4q3RdINAAAAAE8h//QrJEkhl2pJFhdJaTjpdk55S/OjyJgxo7Jly/ZI2yDpBgAAAICnkMVilC3D9/L1Wq07sRmVlnsXO/mufuzv6erq+kgt3PFIugEAAADgKebsFCVnp4uODuOROHl4ODqEFEu7tzoAAAAAAHjCkXQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHbyRCfdsbGxGjx4sAICAuTp6akCBQroo48+kjH/m+DdGKMhQ4Yoe/bs8vT0VN26dXX8+HEHRg0AAAAAwF1PdNI9atQoTZ06VZMmTdLhw4c1atQojR49Wl988YV1ndGjR+vzzz/XtGnTtGvXLnl5ealevXqKinq8k6cDAAAAAHCvJ3rKsF9//VWNGzdWw4YNJUn58uXTN998o927d0u628o9YcIEffDBB2rcuLEkaf78+fL399f333+vli1bOix2AAAAAACe6JbuKlWqaMOGDTp27Jgk6Y8//tC2bdtUv359SdKpU6d08eJF1a1b1/oaHx8fVaxYUTt27HBIzAAAAAAAxHuiW7oHDBig8PBwFS1aVM7OzoqNjdXHH3+s1q1bS5IuXrw7wbu/v7/N6/z9/a3LknL79m3dvn3b+nd4eLgdogcAAAAA/Nc90S3d3333nRYsWKCFCxdq3759mjdvnj777DPNmzfvkbY7cuRI+fj4WP/lzp07lSIGAAAAAOB/nuiku1+/fhowYIBatmypUqVKqU2bNurdu7dGjhwpScqWLZsk6dKlSzavu3TpknVZUgYOHKiwsDDrv+DgYPt9CAAAAADAf9YTnXRHRkbKyck2RGdnZ8XFxUmSAgIClC1bNm3YsMG6PDw8XLt27VLlypXvu113d3d5e3vb/AMAAAAAILU90X26X3zxRX388cfKkyePSpQoof3792vcuHF64403JEkWi0W9evXSiBEjVKhQIQUEBGjw4MHKkSOHmjRp4tjgAQAAAAD/eU900v3FF19o8ODBeuuttxQSEqIcOXKoa9euGjJkiHWd/v376+bNm+rSpYuuX7+uatWqafXq1fLw8HBg5AAAAAAASBZjjHF0EI4WHh4uHx8fhYWFPXGPmsddLOzoENIUp2zHHB1CmkL9Sh7qV/JRx5KHOpY81K/koX4lD/Ur+ahjyUMdS54nsX49bB75RPfpBgAAAAAgLSPpBgAAAADATki6AQAAAACwE5JuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOnuh5ugEAaVu9HKUdHUKasi7O0REAAIDURks3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ0wZRgAAEiTmJIueZiSDgAcg5ZuAAAAAADshKQbAAAAAAA7IekGAAAAAMBOSLoBAAAAALATBlID/sMYhCh5GIQIAAAAyUVLNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJ/TpBgAAAO7BuCfJx9gnQNJo6QYAAAAAwE5IugEAAAAAsBOSbgAAAAAA7ISkGwAAAAAAOyHpBgAAAADATki6AQAAAACwE5JuAAAAAADshHm6AQAAAOAxYy745EnL88DT0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnTB6+ROOUQ2TJy2PaggAAADg6UNLNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJylOuq9fv66ZM2dq4MCBCg0NlSTt27dP586dS7XgAAAAAABIy1xS8qIDBw6obt268vHx0enTp9W5c2dlzpxZy5Yt05kzZzR//vzUjhMAAAAAgDQnRS3dffr0Ufv27XX8+HF5eHhYyxs0aKCtW7emWnAAAAAAAKRlKUq69+zZo65duyYqz5kzpy5evPjIQQEAAAAA8DRIUdLt7u6u8PDwROXHjh2Tr6/vIwcFAAAAAMDTIEVJ90svvaThw4frzp07kiSLxaIzZ87ovffeU7NmzVI1wHPnzun1119XlixZ5OnpqVKlSum3336zLjfGaMiQIcqePbs8PT1Vt25dHT9+PFVjAAAAAAAgJVKUdI8dO1YRERHy8/PTrVu3FBQUpIIFCypDhgz6+OOPUy24a9euqWrVqnJ1ddXPP/+sv/76S2PHjlWmTJms64wePVqff/65pk2bpl27dsnLy0v16tVTVFRUqsUBAAAAAEBKpGj0ch8fH61bt07btm3TgQMHFBERobJly6pu3bqpGtyoUaOUO3duzZkzx1oWEBBg/X9jjCZMmKAPPvhAjRs3liTNnz9f/v7++v7779WyZctUjQcAAAAAgORI8TzdklStWjW99dZb6t+/f6on3JK0cuVKlStXTq+++qr8/Pz07LPP6ssvv7QuP3XqlC5evGjz3j4+PqpYsaJ27NiR6vEAAAAAAJAcKWrp/vzzz5Mst1gs8vDwUMGCBVWjRg05Ozs/UnAnT57U1KlT1adPHw0aNEh79uzRO++8Izc3N7Vr1846Urq/v7/N6/z9/R84ivrt27d1+/Zt699JDQoHAAAAAMCjSlHSPX78eF2+fFmRkZHW/tXXrl1TunTplD59eoWEhCh//vzatGmTcufOneLg4uLiVK5cOX3yySeSpGeffVZ//vmnpk2bpnbt2qV4uyNHjtSwYcNS/HoAAAAAAB5Gih4v/+STT1S+fHkdP35cV69e1dWrV3Xs2DFVrFhREydO1JkzZ5QtWzb17t37kYLLnj27ihcvblNWrFgxnTlzRpKULVs2SdKlS5ds1rl06ZJ1WVIGDhyosLAw67/g4OBHihMAAAAAgKSkKOn+4IMPNH78eBUoUMBaVrBgQX322WcaOHCgcuXKpdGjR2v79u2PFFzVqlV19OhRm7Jjx44pb968ku4OqpYtWzZt2LDBujw8PFy7du1S5cqV77tdd3d3eXt72/wDAAAAACC1pejx8gsXLigmJiZReUxMjLUvdY4cOXTjxo1HCq53796qUqWKPvnkEzVv3ly7d+/WjBkzNGPGDEl3+5D36tVLI0aMUKFChRQQEKDBgwcrR44catKkySO9NwAAAAAAjypFLd21atVS165dtX//fmvZ/v371a1bN9WuXVuSdPDgQZvpvVKifPnyWr58ub755huVLFlSH330kSZMmKDWrVtb1+nfv7/efvttdenSReXLl1dERIRWr14tDw+PR3pvAAAAAAAeVYpaumfNmqU2bdooMDBQrq6uku62ctepU0ezZs2SJKVPn15jx4595AAbNWqkRo0a3Xe5xWLR8OHDNXz48Ed+LwAAAAAAUlOKku5s2bJp3bp1OnLkiI4dOyZJKlKkiIoUKWJdp1atWqkTIQAAAAAAaVSKku54RYsWVdGiRVMrFgAAAAAAniopTrrPnj2rlStX6syZM4qOjrZZNm7cuEcODAAAAACAtC5FSfeGDRv00ksvKX/+/Dpy5IhKliyp06dPyxijsmXLpnaMAAAAAACkSSkavXzgwIHq27evDh48KA8PDy1dulTBwcEKCgrSq6++mtoxAgAAAACQJqUo6T58+LDatm0rSXJxcdGtW7eUPn16DR8+XKNGjUrVAAEAAAAASKtSlHR7eXlZ+3Fnz55dJ06csC67cuVK6kQGAAAAAEAal6I+3ZUqVdK2bdtUrFgxNWjQQO+++64OHjyoZcuWqVKlSqkdIwAAAAAAaVKKku5x48YpIiJCkjRs2DBFRETo22+/VaFChRi5HAAAAACA/5eipDt//vzW//fy8tK0adNSLSAAAAAAAJ4WKerTnT9/fl29ejVR+fXr120ScgAAAAAA/stSlHSfPn1asbGxicpv376tc+fOPXJQAAAAAAA8DZL1ePnKlSut/79mzRr5+PhY/46NjdWGDRuUL1++VAsOAAAAAIC0LFlJd5MmTSRJFotF7dq1s1nm6uqqfPnyaezYsakWHAAAAAAAaVmyku64uDhJUkBAgPbs2aOsWbPaJSgAAAAAAJ4GKRq9/NSpU6kdBwAAAAAAT50UJd2StGHDBm3YsEEhISHWFvB4s2fPfuTAAAAAAABI61KUdA8bNkzDhw9XuXLllD17dlksltSOCwAAAACANC9FSfe0adM0d+5ctWnTJrXjAQAAAADgqZGiebqjo6NVpUqV1I4FAAAAAICnSoqS7k6dOmnhwoWpHQsAAAAAAE+VFD1eHhUVpRkzZmj9+vV65pln5OrqarN83LhxqRIcAAAAAABpWYqS7gMHDqhMmTKSpD///NNmGYOqAQAAAABwV4qS7k2bNqV2HAAAAAAAPHVS1Kc73t9//601a9bo1q1bkiRjTKoEBQAAAADA0yBFSffVq1dVp04dFS5cWA0aNNCFCxckSR07dtS7776bqgECAAAAAJBWpSjp7t27t1xdXXXmzBmlS5fOWt6iRQutXr061YIDAAAAACAtS1Gf7rVr12rNmjXKlSuXTXmhQoX0zz//pEpgAAAAAACkdSlq6b5586ZNC3e80NBQubu7P3JQAAAAAAA8DVKUdFevXl3z58+3/m2xWBQXF6fRo0erVq1aqRYcAAAAAABpWYoeLx89erTq1Kmj3377TdHR0erfv78OHTqk0NBQbd++PbVjBAAAAAAgTUpRS3fJkiV17NgxVatWTY0bN9bNmzfVtGlT7d+/XwUKFEjtGAEAAAAASJNS1NItST4+Pnr//fdTMxYAAAAAAJ4qKWrpnjNnjhYvXpyofPHixZo3b94jBwUAAAAAwNMgRUn3yJEjlTVr1kTlfn5++uSTTx45KAAAAAAAngYpSrrPnDmjgICAROV58+bVmTNnHjkoAAAAAACeBilKuv38/HTgwIFE5X/88YeyZMnyyEEBAAAAAPA0SFHS3apVK73zzjvatGmTYmNjFRsbq40bN6pnz55q2bJlascIAAAAAECalKLRyz/66COdPn1aderUkYvL3U3ExcWpbdu29OkGAAAAAOD/JTvpNsbo4sWLmjt3rkaMGKHff/9dnp6eKlWqlPLmzWuPGAEAAAAASJNSlHQXLFhQhw4dUqFChVSoUCF7xAUAAAAAQJqX7D7dTk5OKlSokK5evWqPeAAAAAAAeGqkaCC1Tz/9VP369dOff/6Z2vEAAAAAAPDUSNFAam3btlVkZKRKly4tNzc3eXp62iwPDQ1NleAAAAAAAEjLUpR0T5gwIZXDAAAAAADg6ZOipLtdu3apHQcAAAAAAE+dFPXplqQTJ07ogw8+UKtWrRQSEiJJ+vnnn3Xo0KFUCw4AAAAAgLQsRUn3li1bVKpUKe3atUvLli1TRESEJOmPP/7Q0KFDUzVAAAAAAADSqhQl3QMGDNCIESO0bt06ubm5Wctr166tnTt3plpwAAAAAACkZSlKug8ePKiXX345Ubmfn5+uXLnyyEEBAAAAAPA0SFHSnTFjRl24cCFR+f79+5UzZ85HDgoAAAAAgKdBipLuli1b6r333tPFixdlsVgUFxen7du3q2/fvmrbtm1qxwgAAAAAQJqUoqT7k08+UbFixZQnTx5FRESoePHiqlGjhqpUqaIPPvggtWMEAAAAACBNStY83XFxcRozZoxWrlyp6OhotWnTRs2aNVNERISeffZZFSpUyF5xAgAAAACQ5iQr6f7444/14Ycfqm7duvL09NTChQtljNHs2bPtFR8AAAAAAGlWsh4vnz9/vqZMmaI1a9bo+++/16pVq7RgwQLFxcXZKz4AAAAAANKsZCXdZ86cUYMGDax/161bVxaLRefPn0/1wAAAAAAASOuSlXTHxMTIw8PDpszV1VV37txJ1aAAAAAAAHgaJCvpNsaoffv2atq0qfVfVFSU3nzzTZsye/n0009lsVjUq1cva1lUVJS6d++uLFmyKH369GrWrJkuXbpktxgAAAAAAHhYyRpIrV27donKXn/99VQL5kH27Nmj6dOn65lnnrEp7927t3788UctXrxYPj4+6tGjh5o2bart27c/lrgAAAAAALifZCXdc+bMsVccDxQREaHWrVvryy+/1IgRI6zlYWFhmjVrlhYuXKjatWtbYyxWrJh27typSpUqOSReAAAAAACkZD5e7ijdu3dXw4YNVbduXZvyvXv36s6dOzblRYsWVZ48ebRjx477bu/27dsKDw+3+QcAAAAAQGpLVku3IyxatEj79u3Tnj17Ei27ePGi3NzclDFjRptyf39/Xbx48b7bHDlypIYNG5baoQIAAAAAYOOJbukODg5Wz549tWDBgkSjpj+KgQMHKiwszPovODg41bYNAAAAAEC8Jzrp3rt3r0JCQlS2bFm5uLjIxcVFW7Zs0eeffy4XFxf5+/srOjpa169ft3ndpUuXlC1btvtu193dXd7e3jb/AAAAAABIbU/04+V16tTRwYMHbco6dOigokWL6r333lPu3Lnl6uqqDRs2qFmzZpKko0eP6syZM6pcubIjQgYAAAAAwOqJTrozZMigkiVL2pR5eXkpS5Ys1vKOHTuqT58+ypw5s7y9vfX222+rcuXKjFwOAAAAAHC4Jzrpfhjjx4+Xk5OTmjVrptu3b6tevXqaMmWKo8MCAAAAACDtJd2bN2+2+dvDw0OTJ0/W5MmTHRMQAAAAAAD38UQPpAYAAAAAQFpG0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB28kQn3SNHjlT58uWVIUMG+fn5qUmTJjp69KjNOlFRUerevbuyZMmi9OnTq1mzZrp06ZKDIgYAAAAA4H+e6KR7y5Yt6t69u3bu3Kl169bpzp07ev7553Xz5k3rOr1799aqVau0ePFibdmyRefPn1fTpk0dGDUAAAAAAHe5ODqAB1m9erXN33PnzpWfn5/27t2rGjVqKCwsTLNmzdLChQtVu3ZtSdKcOXNUrFgx7dy5U5UqVXJE2AAAAAAASHrCW7rvFRYWJknKnDmzJGnv3r26c+eO6tata12naNGiypMnj3bs2HHf7dy+fVvh4eE2/wAAAAAASG1pJumOi4tTr169VLVqVZUsWVKSdPHiRbm5uSljxow26/r7++vixYv33dbIkSPl4+Nj/Zc7d257hg4AAAAA+I9KM0l39+7d9eeff2rRokWPvK2BAwcqLCzM+i84ODgVIgQAAAAAwNYT3ac7Xo8ePfTDDz9o69atypUrl7U8W7Zsio6O1vXr121auy9duqRs2bLdd3vu7u5yd3e3Z8gAAAAAADzZLd3GGPXo0UPLly/Xxo0bFRAQYLM8MDBQrq6u2rBhg7Xs6NGjOnPmjCpXrvy4wwUAAAAAwMYT3dLdvXt3LVy4UCtWrFCGDBms/bR9fHzk6ekpHx8fdezYUX369FHmzJnl7e2tt99+W5UrV2bkcgAAAACAwz3RSffUqVMlSTVr1rQpnzNnjtq3by9JGj9+vJycnNSsWTPdvn1b9erV05QpUx5zpAAAAAAAJPZEJ93GmH9dx8PDQ5MnT9bkyZMfQ0QAAAAAADy8J7pPNwAAAAAAaRlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANgJSTcAAAAAAHZC0g0AAAAAgJ2QdAMAAAAAYCck3QAAAAAA2AlJNwAAAAAAdkLSDQAAAACAnZB0AwAAAABgJyTdAAAAAADYCUk3AAAAAAB2QtINAAAAAICdkHQDAAAAAGAnJN0AAAAAANjJU5N0T548Wfny5ZOHh4cqVqyo3bt3OzokAAAAAMB/3FORdH/77bfq06ePhg4dqn379ql06dKqV6+eQkJCHB0aAAAAAOA/7KlIuseNG6fOnTurQ4cOKl68uKZNm6Z06dJp9uzZjg4NAAAAAPAfluaT7ujoaO3du1d169a1ljk5Oalu3brasWOHAyMDAAAAAPzXuTg6gEd15coVxcbGyt/f36bc399fR44cSfI1t2/f1u3bt61/h4WFSZLCw8PtF2gKxZg7jg4hTXkS9+GTjPqVPNSv5KOOJQ91LHmoX8lD/Uoe6lfyUceShzqWPE9i/YqPyRjzwPXSfNKdEiNHjtSwYcMSlefOndsB0SA1+fj4ODoEPMWoX7A36hjsifoFe6OOwZ6e5Pp148aNB8aX5pPurFmzytnZWZcuXbIpv3TpkrJly5bkawYOHKg+ffpY/46Li1NoaKiyZMkii8Vi13ifBuHh4cqdO7eCg4Pl7e3t6HDwlKF+wd6oY7An6hfsifoFe6OOJY8xRjdu3FCOHDkeuF6aT7rd3NwUGBioDRs2qEmTJpLuJtEbNmxQjx49knyNu7u73N3dbcoyZsxo50ifPt7e3vwYYTfUL9gbdQz2RP2CPVG/YG/UsYf3MC3waT7plqQ+ffqoXbt2KleunCpUqKAJEybo5s2b6tChg6NDAwAAAAD8hz0VSXeLFi10+fJlDRkyRBcvXlSZMmW0evXqRIOrAQAAAADwOD0VSbck9ejR476PkyN1ubu7a+jQoYke0QdSA/UL9kYdgz1Rv2BP1C/YG3XMPizm38Y3BwAAAAAAKeLk6AAAAAAAAHhakXQDAAAAAGAnJN0AAAAAANgJSTcAPEHi4uIcHQIAAABSEUk3/tMYRxBPEmOMnJzuHpY3btxIAo7HjjqXtnAOg6NwrACSh6Qb/1lxcXGyWCySpJMnT3ICgUMZY6z1cciQIerTp4/+/vtvB0eF/4L4xO3XX3/V8uXLdeXKFQdHhIcRf8zYvn27Vq9erdu3bzs6JPxHxMXFWW8QL168WBs3bnRwRHgaxJ+L7ty54+BI7IOkG/9JCU8YH374oV5//XXt3r1bsbGxDo4M/1XxCffBgwe1b98+ffHFFypcuLCDo8LTLj5xW7ZsmRo0aKDDhw8rLCzM0WHhXyTcb40bN9aGDRu4WYLHIuETWe+9954GDhyoffv26erVqzx5gRSLP6atWbNG3bt3V0hIiKNDSnUujg4AcIT4E8bAgQM1d+5cTZo0SXnz5pWzs7ODI8N/2eTJk7V06VLFxMSoWLFikmxbwIHUZrFYtHnzZr3xxhsaN26c2rZtKxeXu5cG0dHRcnNzc3CESIrFYtHGjRvVrl07TZo0SS1btpS7u7ujw8J/QPz56NNPP9Xs2bP1ww8/qGLFig6OCmmdxWLR0qVL1alTJ7Vv314XLlyQn5+fo8NKVSTd+M/67bfftGjRIi1atEhBQUGKiorS+fPn9fvvvysgIMCa9ACPS6FChXTs2DGFhYVp7969qlevniwWC4k37Grt2rWqU6eO3njjDUVGRmrnzp2aO3eunJ2d1aRJE9WvX9/RISIJGzZsULNmzdSuXTvdvHlTv/32m+bOnassWbKoRo0aatCggaNDxFPq2rVr2rhxo8aMGaOKFSvq9OnT+uuvvzR37lzlz59fAwYMUMaMGR0dJtKQP/74Q2+++abGjBmjTp06WcuvX78ub29va2NZWkbSjf+s8PBwWSwWFS1aVLt27dKSJUu0atUqXbx4UeXKldOnn36qcuXKOTpMPKUSdnGQ7rZoP//88/rmm2/Utm1bzZgxQ35+fnr22WdJvGFXsbGxunz5sr777jstW7ZMN27cUGhoqHLlyqUBAwbomWeeUc6cOR0dJu5x7tw57dmzRzt27NDEiRMVGhqqmzdvKjY2VgcPHlSlSpWUOXNmR4eJp1DGjBkVExOj5cuXy8/PT1OnTtW1a9eUI0cOTZkyRWFhYZo6daqjw0QacvLkSRUtWlSdOnVSaGiofvzxRy1cuFCHDx9Whw4d9Pbbb6f541nav20APISkBkkrV66cQkNDVa9ePdWtW1c3btzQxx9/rC1btujAgQM6c+aMAyLFf0HChHvlypWaOnWqxo0bp0uXLql69eqaM2eO9u3bp7Fjx+r333+XJBJupIqEfS7jx7B47bXXFBcXp3fffVeurq7q0aOHduzYodatW8vT01NeXl6OChf/L+F+i///kSNHSpKaNGkii8Winj17avv27Ro4cKCCg4PpX4tUkdT1k8ViUZcuXRQSEqJXXnlFzzzzjEaOHKnvvvtOffv2VWhoKGPk4F8lPEZlzJhR27dv1+DBg1W/fn0tWbJEhQoVUseOHfXpp5/qxIkTDow0ddDSjadewgRn27Zt8vDwkLu7u0qVKqVDhw5p4cKFKlWqlKpXry4vLy/FxcWpQIECnDBgN/H1sX///lq6dKny5MkjFxcXDRgwQBs2bFDNmjU1e/ZsdezYUePHj1f37t1VoUIFB0eNtC7+aYm1a9fqp59+0t69e9WqVSs1aNBAW7ZsUXBwsPLly2ddf/fu3XJ2diZ5c7D4/bZx40atWLFCp0+fVvXq1dW5c2f9/vvvOnXqlM2gi7/++quyZMli7ZsPpFTC66dZs2ZZB5ytUKGCunTpombNmuns2bMKCAiwvmbTpk165plnGCMH9xV/TLt+/brc3d0VGxurWrVqacqUKZozZ44qV66sDh06qHTp0pLuNk6Eh4c7OOpUYID/iH79+pmsWbOaXLlymQIFCpgFCxYYY4yJi4szxhhz69Ytc/nyZVO/fn0TGBhoYmJiHBkunnLz5883/v7+5rfffjPGGLN06VJjsVjMkiVLrOts2LDBeHp6mmHDhjkqTDxlli9fbry8vEyfPn3MwIEDTdmyZU3FihXNqVOnrOts3brV9O3b13h7e5vff//dccHCatmyZcbb29u88cYb5pNPPjEeHh7mxRdfNOfOnbOus2nTJtO/f3/2G1Jdv379TPbs2U2fPn3MBx98YCwWi+nRo4d1eUREhNmyZYupV6+eeeaZZ8ydO3ccGC2eZPHX3KtWrTLVq1c3gYGBpmjRombp0qXGGGOioqJs1h84cKDJnz+/zbEurSLpxlMr/odtjDEHDx40hQoVMrt27TLr1q0z/fr1MxaLxcyfP98YY0x0dLSZNGmSqVixoqlcubKJjo42xhgSb6SKzZs3JyobPny46du3rzHGmMWLF5sMGTKY6dOnG2OMuX79ugkLCzPGGPPbb79RD5Eqzp49a8qWLWumTJlijDEmMjLSeHt7m/79+1vXuXz5smnRooWpVq2a+eOPPxwVKhIIDg42JUuWNJMmTTLG3D23ZcyY0Xr8MObuvu3SpYspX748+w2pauvWrSZ//vxm+/btxpi7N4A8PDzM1KlTreusWbPGtGnTxjRq1Mh6/UTijfv58ccfjaenpxkzZozZv3+/6dSpk7FYLGbHjh3Wa/eVK1eadu3aGV9fX7Nv3z4HR5w6ePYIT634PrDjx4/XuXPn1Lx5c+sjus8++6xcXFzUrl07WSwWvf7666pVq5ZiY2PVvXt3OTs7KyYmhsfz8Mi++eYbtW7dWvPmzVObNm2s5ZcuXVJkZKR+/PFHvfHGGxo1apS6dOkiSfr666919uxZDRs2TIGBgZLu9r/lcT08qjt37ujVV1/ViRMnFBQUpBYtWmjUqFGSpC1btqhq1aoaP368nJ2dn7rpWtIqY4w8PDzUuXNnnTx5UtWrV9err76qMWPGSJL27t2rwMBADR06VC4uLuw3pKpz587Jz89PVapU0fLly9W2bVtNnDhRXbp0UXh4uA4cOKDnn39eOXPmVLFixeTk5MT1E+7rzp07mjt3rt5991317dtXwcHB2rJlizp37qxKlSpZ17l+/bqioqK0efNmFS9e3MFRpw4GUsNT7dq1a/rtt980YcIEBQcHW8uzZMmid999VwMGDFD79u01Y8YMFS9eXO+8846cnZ0VGxvLCQOpomHDhvrggw/UoUMHzZs3z1peq1YtHTx4UK+88oo+/vhjdevWTdLdUfV//vlnxcXF2cyRTMKNlDD/3x/bGKPw8HDFxMTo8OHDev755/XCCy9o2rRpkqQ///xTs2fP1t69e5U9e3YStydIVFSULly4oJ9++knPP/+8GjZsaB0Z+uDBgxo6dKj27t2rHDlysN+Q6vz9/eXn56eZM2eqbdu2+uyzz6w3iHfv3q0vv/xS586dU4kSJeTk5KS4uDiun3BfMTExOnLkiOrUqaMbN26oUqVKqlWrlqZPny5Jmj59ukJDQ9WmTRvNnj37qUm4JZJuPGXMPQP+ZMqUSYMGDVKnTp20YMECrV692rosS5Ys6tu3r7p27ap58+bJ3O1uIYkEB6nDGCNvb28NGTJEw4cPV4cOHbR06VJJUt26dZU/f37lzJlT6dKlU0hIiA4cOKCWLVvqwoUL+vjjj63bAJIrvt7EjzxssVhUrFgxlShRQkFBQapevbpmzpxpHSRpwYIFOnTokPLkyeOwmJF4v0lSoUKFVL9+fbVo0UIlS5bUjBkzrOeoRYsW6fLly8qRI4dD4sXTI6lRyiXJx8dHR44cUZcuXTR06FB17dpVknTr1i2NGzdOFovFpv49DfMpw348PT0VGBiomTNnqkSJEmrcuLEmTZokSbp586ZWrVqlb775RsYYpUuXzsHRpi6L4YoOT4mEo2yGh4fr1q1b8vf3l3T38aj3339fK1as0KJFi1SvXj3r68LDw5UhQwbmQkaqSliXJk+erJCQEH300UeSZH3U/OrVq+rYsaNOnjypY8eOqXTp0vL09NS6devk6urKI+VIkfi6t379en333XfKkSOHatasqZo1a+rMmTPq0KGDjh49qunTp+v69evas2ePZs+erV9++cU6Wiwev/j9tmHDBq1fv14uLi568803lTNnTv36668aPHiwbty4oQ8++EDS3VGi4/fbM8884+DokZYlPF9NmzZNZ8+elbu7uwYOHCgXFxctWbJELVq00JtvvqmqVasqU6ZMGjdunEJCQrR37165uLhw/YRE4uvEzZs3FRcXpwwZMki6OxL+iBEjlC1bNm3ZssX6VN+gQYO0ZMkSrVmzxmZE/KfG4+1CDthHwkHTPvroI1O5cmWTK1cu88ILL5iffvrJxMTEmPPnz5sOHTqYTJkymbVr1z5wG0BqGTRokMmWLZv5+uuvzcSJE03z5s2NxWIxs2fPNsbcHfX177//NqtWrTJ//vmniY2NNcYwCA0ezfr16427u7t59dVXTe7cuU1QUJCZPHmyMcaYv//+27z66qsmT548pmTJkqZ+/foMvvWEWLt2rXFzczMvvfSS8fHxMaVLlzYrV640xtydzaBNmzYmXbp05plnnjG1a9dmvyFVDRkyxGTOnNnUrl3b5MuXzxQtWtRcunTJGGPMvHnzTM2aNU3GjBlNtWrVTNOmTRl0Fv/q+++/N0FBQaZs2bJm1KhRJjo62sTExJh3333XPPPMM6ZevXqmf//+pnnz5iZTpkxm//79jg7ZbmjpxlNl2LBh+uKLL/TRRx8pU6ZMmjp1qiIjI9W1a1d17NhR//zzjz755BPNnDlTO3fuZO5jpDqT4G7/lStXVLduXb399tvq2LGjJOny5csaM2aMPvvsMy1YsECtWrVKtI2ET20AKTF27Fi5uLioZ8+eOnXqlEaOHKk//vhDbdq0UY8ePSRJJ06csD4NlD59ekeGi/83fPhweXt7q1evXoqLi1ODBg108eJFDRs2TC+99JIsFov++ecfZc2a1ablCEiJe881nTt3VpcuXVS2bFkdOXJEnTp1UkhIiH799Vf5+/srNDRUkZGR8vLyUsaMGWWxWBg0Dfe1Z88eNWjQQG3atJHFYtGkSZP06quvavLkyfLy8tKCBQu0Zs0aXblyRcWKFdObb76pYsWKOTpsu+FXgqeCMUYXL17U8uXL9fnnn+u1116TJDVt2lSdO3fWF198oSpVqqh48eLq1auX8ufPr7Jlyzo4ajxtEl7AnDx5Urlz59bp06dt1vH19dU777yjDRs2qE2bNrp165beeOMNm3VIuJFc8Td7Dh8+LEk6e/asypcvL0kKCAjQ+++/r08++URff/21YmJi1KtXLxUoUMCRIUP/22/Hjx9XdHS0oqOjVahQIUl3jwPff/+9Xn75ZQ0dOlQxMTFq2LCh8ubN6+Co8TRIeL46cOCAbt68qQsXLsjLy0vOzs4qUaKE5s+fr7Zt26pq1ar69ddf5efnp8yZM1u3YYwh4YZVfDtufMNDXFyc3nrrLQ0bNkyS1KxZM9WuXVuxsbGaNGmS2rVrp3bt2jks3seNKzukWQkf0rBYLHJ2dlZERIS1D+zt27fl5uamefPmKSIiQrNmzZIkFS9eXAMGDJCLi4tiYmIcEjuePsYY6wXMe++9p3bt2ikmJkaNGzfW4sWL9c8//1jXzZUrl0qWLKnChQtrzpw5DJaGR2axWLR48WJVrVpV1apV04wZM7Rt2zbr8rx58+r9999XYGCgpk2bZh21HI6VcL/VrVtXn3zyidauXWsd1MrDw0MrVqxQ7ty51a9fP61Zs8bBEeNpkfB8FRQUpM6dO2v9+vU6d+6cdZ1ChQpp/vz5ypYtmwoWLKhr167ZbIM+3LiXxWLRjh07NG3aNH3++ec2A/RVqVJFGzdu1PLly9WnT59EjRJPO5JupElxcXHWg/3169clSd7e3vL09NTPP/8sSXJ3d9edO3ckSeXLl9etW7cSbYc7tEgNCR8p37Fjh3bt2qXPPvtMnp6eqlevnsLDwzV+/HidP39e0t0ROq9fv64xY8Zo69at1kH8gOSKrzfxdWzMmDFavny5unTpop9++snawiBJefLkUd++fdWgQQO98MILjgr5P8skmL4t/v+Dg4M1adIkDRkyRCtXrlSzZs20detWTZkyxXr+cnNz09KlSxUYGMiAaXhksbGx1v9fs2aNfvzxR3311Vf69NNPVaVKFXXp0kV//vmndZ1ChQrpyy+/VKtWreTt7e2IkJFGWCwW/fjjj6patarmzZunb775Rj/88IN27dplXadKlSravHmztc4lrI9PO/p0I81J+EjU2LFj9ddff2nAgAEqVKiQVq9eraZNm6p37942Uy5VrFhR9erVs44eDdjDwoULtWLFCsXFxWnRokXWpy7Gjx+vxYsX6/LlyypfvrwOHz4sY4x+++03Rn1FsoWFhcnHx8f696ZNmzRz5kx5eHho/Pjx8vb21sWLFzVt2jR9++23atmypYYOHWpdnz6YjnH27FnlypXL+ve+ffs0efJk3b59W9OnT5eXl5ciIyP11ltv6ciRI2rdurXefPNNubq6OjBqPC3urX8zZszQ+fPn5eTkpCFDhki6e/OucePG+ueff7Ry5UqVLFky0XaYVQMJxcbGysnJSRaLRcHBwRo2bJgqVaqk9u3ba9euXWrdurWqVq2qPn36KDAw0Pq6PXv2KH369E91H+570dKNNCc+4e7Xr5/GjBmjWrVqWU8ANWrU0Lhx4zR27FjVqlVLrVu3VlBQkCIiImwuOoFHlfB+ZfzjU9u2bdP69ev1+++/68aNG9blvXv31qhRo9SpUydZLBbVrl1be/bskYuLi2JjY0m48dDix6eIiYlRXFyc7ty5o7/++ks//fSTNm/ebG2JypYtm7p27aoWLVpo6dKl6t+/v3UbJNyP38yZM/Xaa6/p1q1biomJUUxMjHUQob1798rLy0uSlC5dOk2aNElFihTRd999p/Hjx1tbvIGUaty4sRYuXCjpf+eumTNnavjw4Tp06JC1q523t7dWrFihfPny6eWXX9b+/fsTbYuEG5L0/fffKyoqSs7OzrJYLNq9e7f69Omjw4cPq0qVKnJxcVHVqlU1f/587dixQ5999plNfSpfvvx/KuGWxJRhSJu+/fZbkzNnTrNnzx5r2e3bt82ZM2eMMcbs27fPtGnTxnTo0MH06dPHOv0S01ogtVy+fNkEBweb48ePm7Nnz1rLR4wYYXLnzm3effddExISkuh1CaemY1owJNepU6fMsWPHjDHGREVFGWOMuXTpkpk2bZrx8PAwvXr1sln//Pnzpl+/fqZChQrm8uXLjz1e3LV582Zz8uRJY4wx169fN8YYc/XqVTN48GCTI0cO07dvX5vjwY0bN8zLL79snnvuORMaGuqQmPH0WLVqlbl9+7YxxpgrV65Yy5s2bWp8fHzMzz//bJ3+yxhjwsPDTcmSJc0rr7zy2GPFk+/nn382tWrVMufOnbOWbdy40ZQuXdp4enqar776ymb9rVu3msKFC5uGDRua33///XGH+8Qg6UaaED93cbxJkyaZ6tWrG2OMOXTokBkzZowpWrSo8fHxMYMHD05yGyQ4SC3ffPONqVGjhsmRI4dxcnIyuXPnNu+//751+aBBg8yzzz5rBg8ebL3AubcOA49iz549Jnv27Oaff/4xxtxN4CZNmmQyZcpk+vbta7PuhQsXSLifEPv27TNlypQx27dvN8YYc+3aNdO/f39TqVIl8/7779scJyIiImwuaoHkiouLs6lT48aNMx06dDCHDh2yltWpU8fkzJnTrFu3zuY66ebNmzRUIEm3bt0yFy5cMMYY89dff5mbN28aY4zZuXOnqVKlinn++efN2rVrbV6zYcMGU6ZMGZtGiv8a+nQjTRk4cKAyZcqkYsWKqWvXrqpYsaIOHTqkChUqqGzZsvLx8VHnzp114MCBJPsiAY9q9uzZ6tGjhz799FOVKlVKERERWr58uebNm6dXX31V8+fPl5ubm/r376+NGzeqUaNGeuutt+Tn5+fo0JHGmf/v+3/06FHdunVL3bp10+XLl7V582blypVLV69e1bfffqvBgwerc+fO+vTTTx0dMvS/cUjOnTun33//XZ9//rnCwsL0+eefq0KFCgoNDdWnn36qrVu3ql69eho6dCjTBiJVmATjhVy5ckXbt29X586d1aZNG3Xq1Mn6eG+dOnV07NgxzZs3TzVq1LDpgkIfbiSUcEyQEydOqGXLlgoMDNS4ceOULl06bd26Ve+//74yZ86sHj166LnnnrO+9tatW/L09HRU6I7n2JwfeLCEd2hXrlxpChQoYPbs2WMuXrxo5syZY5o0aWJmz55tTp8+bYy5e8etcuXK5vjx444KGU+xvXv3mvz585tFixbZlF+9etVMmTLFuLm5mR49eljLBw4caHLlymW+/PLLxx0qnlLLly83xYsXNzt37jQnTpwwNWrUMHny5DHBwcHGmLuPjk6dOtVYLJb7PvWDx2/RokWmUKFC5sSJE2bdunXmpZdeMoGBgWbXrl3GmLvHkAEDBpjChQubESNGODhaPA0SdmXq2bOnKVasmDHGmK+++srkzJnT9OzZ0/z111/WdZ577jnj7Oxs020PuFd8vdqxY4c5e/asGTBggKlWrZrp1auXtcV78+bNplq1aqZp06bmhx9+SPTa/yqSbqQJa9asMV27djXDhw+3KY9/9CkmJsZERESYhg0bmlq1avEoL+xi8eLFpkyZMubChQsmLi7O5gQSHh5uBg8ebNzc3MzmzZut5VOnTuURPTyS+Hp29epV07hxYzNx4kTrsqQS75CQEDNz5kxz5MgRh8SLu+L3261bt0ybNm3M2LFjrcs2btxoGjdubMqWLWt27txpjLl7w2To0KHm1KlTjggXT5GE56b9+/ebhg0bmm3btlnL5s+fn2Ti/c4773C+wr9av369sVgsZvPmzebGjRtm6NChpmLFijaJ95YtW0zJkiXNa6+9Zi37r+PxcjzxTp06pYYNG+r06dPq2LGjvvjiC0n/e2QvMjJSK1eu1PTp0xUeHq6dO3fK1dXVZmoxIDW89957WrJkiU6cOCFJiab62r9/vypVqqRvv/1WTZo0sXktj+jhYd1bryRp/fr1GjNmjIwxGjdunE33mZMnT6pDhw46d+6cNmzYoLx583L8c4Ck9tumTZvUr18/+fr6avTo0SpVqpTNsokTJ+rixYsaO3asqlatyn5Dqvrmm280c+ZMubm56fvvv5fFYpGbm5sk6auvvtKgQYP06quvqn379jZzwHO+wv2cPn1au3bt0vnz59W7d29JUlRUlD799FOtXr1alStX1scff6x06dJp+/btypkzp/Lly+fYoJ8QHNnxxIm/DxT/34CAAE2ePFnPPPOMNm7cqLVr10r639RhERERunbtmipVqqRdu3bJ1dVVMTExXLgg1ZUtW1bnz5+31sF7L7CfffZZZcyYUVeuXEn0Wi5g8DDi4uJksVh09epV/fHHHzpy5IgkKW/evPr111+1fv16nTp1yuY1+fPn19y5c+Xl5aUXX3xRMTExTEP3mCXcb4cPH9avv/4qSfLy8lJERITWr19vnZYpfgqwWrVqqVevXvL09NQHH3ygqKgo9hseyT///KMDBw5YbwwfPnxYwcHB+uuvv2SMkZubm7X+tWnTRiNHjtTnn3+uTZs22WyH8xXip0JN6O+//1bp0qX11ltvWftm37lzRx4eHnrvvff0wgsvaM+ePerZs6du3bqlqlWrknAnwGSdeKIkvMsfEhIiY4x8fX1Vq1YtjRkzRv3799f06dPl5uammjVrSpL8/PzUvn176wEgNjaWeWhhF4ULF5a7u7vmzp2r/Pnzq2DBgpL+V2+PHj2q7Nmzq2jRog6OFGlRfD3666+/1K1bN7m5uSlTpkz66quvVKhQIR06dEjlypXT2LFjVaRIERUuXNj62oCAAK1cuVIS83A/bgn32zvvvCNvb2/lypVLFSpUULly5fTVV1+pTZs26t69u7Zs2WK9Mezi4qKaNWvK2dlZAQEB8vDwcPRHQRq2cOFCzZgxQ9HR0erVq5cKFCigoUOHKmvWrJowYYLefPNNjR8/XpkyZdKdO3fk6uqq119/XVmzZrUZ7ApIOPjj1q1bdfnyZTVv3lze3t7q06ePPv/8cx07dkyS5Orqqjt37sjT01MDBgxQZGSk9u/fr/Dw8P/2oGlJcdRz7cC9EvZBGjFihKlQoYIpXry4KVWqlNm4caMx5m4/uMqVK5tmzZrZ9JsFHpdp06YZi8Vi2rVrZx0EyZi706s0atTIBAUFMaYAki3++Hfw4EGTKVMmM2jQIHPixAlrXYqfyufvv/82mTJlMvXr12fAyCdAwv2WMWNGM3DgQHPw4MFE6+3evdsEBASYoKAga5/ZhPMiA49izpw5xtvb2yxYsMBmOjBj7g5IO2rUKFO5cmXTrVs36zzx99Y/+nLDmP8NYHzgwAHzzDPPmObNm5uhQ4eaGzduGGOMuXjxohk+fLhxcXGxGacivj7dunWLKSrvg6QbT5yhQ4caPz8/s3jxYnPmzBlTunRpU7BgQesI5Rs2bDDVqlUzNWvWNPv27XNwtPivSHhTaMKECcbZ2dnkyZPHtGrVyrz++uumZs2apnTp0tYTD4k3kuvy5cumUqVK5p133rEpj6978RfFx48fN5kyZTIvvvgig6U9AS5fvmzKlSuXaL/dewyIT7zr1KlDgoNUs2PHDpM7d+5Es2TExsZa62BMTIz59NNPTeXKlU337t1NaGioI0JFGnH48GGTOXNmM2jQIHPt2rVEy0NCQszw4cONt7e3GTdunLWcG4kPxjNoeGIYY3T58mWtW7dO06dPV5MmTfTTTz/p9OnT+vTTT5U3b14ZY1S7dm3dvHlTP/74o0qXLu3osPGUud9ARgn7Wvbs2VMlS5bUypUrtWPHDhUuXFjPPfec+vfvLxcXF5t5LIGHdfbsWV29elUtW7a0qYfxdc/JyUnGGBUsWFA7d+5U0aJF5eHhoQULFsjV1dWRof+nnTlzRlFRUXrttdds9lv8f83/D7BWvnx5LV68WLVq1VLjxo31ww8/ODJspHHx9Wr37t0qWLBgosE74+tf/KBo/fr1k6urq6ZMmaJ8+fKpb9++DogaT7pbt25p0KBBatCggT766KNExzFJ8vX1VZcuXWSxWDRixAhFRkbq/fff5zz0L7gqhEOFhoYqMjJSOXLkkMVi0Z07d3TixAk999xzWrdunVq0aKExY8bozTffVEREhCZPnqyePXvqxRdf1Isvvijp/kkSkFwJ69KaNWuUP39+FSpUyGad+BNPnTp1VKdOnUTbYEwBpNS+ffsUHBysypUrS0o8GrbFYlFkZKSOHz+u0qVL68SJE4qOjuZCx8H279+vv//+W6VKlbrvDbubN29q7969qlGjhjZv3ixvb28HRIqn0c6dO+Xk5KSsWbMmOmYYY+Ts7KwzZ87IYrGoZ8+e8vPzU6tWrRwYMZ5kkZGROnLkiPr3729zPIuvV/E3cfz9/fXuu+/q5s2bmj59urp166ZMmTIxGOQDkKnAYRYtWqSGDRsqMDBQQUFB+uGHH5QzZ04FBgaqc+fOatq0qXXwD+nuwGorV67UunXrJP1vdHMSbqQGY4y1Lg0YMEC9e/fW4sWLFRkZabPevRc05p5ZFxn1FSmVL18+GWP0008/SUo8Or4kTZ06VYMGDdKtW7cUEBCgIkWKPO4wcQ9vb285OTnp8OHDkpTomCBJX3/9tb766itJd2dBiB+EEUip+ONDlixZdPLkSd25c0cWi8Vm1GmLxaLo6Gi99957WrdunZydnfX666/L2dlZsbGxjgodT7CQkBCdP39efn5+khKPYu7s7Ky4uDiNHz9e0dHR6tOnj/bv36/MmTOTcP8LshU4xIwZM9SxY0fVq1dPgwYN0rVr19SjRw/t379fFStW1Pr16/XSSy+pY8eOku4+7vL2228rffr0atCggaSkL0iB5Io/ocTXp9GjR2vmzJmaM2eO3n77baVLl+6+r7VYLNRDpJq8efPK09NTX3/9tc6dO2ctNwmmUTx37pzKlSvHSNdPkDp16sjLy0vjxo2TdPe4cG9Cc+DAAeXKlSvJhBxIifi6VLduXYWHh2vw4MHW6VKjo6Ot6928eVM3btxQ5syZbV7PDWIkxdvbWzExMdqxY4ek/3VrSmjt2rXauXOnLBaLfH19lSVLFkeEmubwDCQeu1mzZqlHjx769ttv9fLLL0u6O1+kr6+vNm3apJ49e+ro0aM6dOiQGjZsqAIFCmj//v0KCwvT3r17rXfZaOHGo4qKirJJXsLDw7VlyxYNGzZMFStWtCbk1DfYmzFGBQoU0MSJE9WxY0f5+PioT58+KlSokCwWi6KiovTRRx9p+fLlWrNmDTd7nhBxcXHKlCmTBg0apD59+sjT01NTp061PvJ/69Ytffzxx/rpp5+0bt069htSTXxdqlmzpipXrqwFCxbI09NTQ4cOlZubmyTp0qVL6ty5syIiIqxd8oD7McYoZ86c6tSpk0aNGqVy5cqpcePGiouLk7Ozs7X7wtatW+Xi4sJ1UTKRdOOxMcYoJiZGI0aMUNasWVWuXDnFxsbKYrEoc+bMKl26tMLCwuTj46Px48frhx9+0KpVqxQWFqZatWppyJAhDFKFVNO5c2d5e3tr7NixNuWHDh1SUFCQJNkMiHTr1i2FhoYqZ86cjz1WPN0S3tRp0aKFLl++rH79+mnHjh2qUaOGnJ2d9c8//2jbtm1as2aNzfzccKz4/daqVSuFhoZqzJgx+uOPP1SnTh3rGCW//vqr1qxZwyPlSHVxcXHy8fHRzJkz1bp1a33xxRfW8XD+/vtv7d+/Xzdu3NDu3btpsMC/ir+R07JlS+3du1dt2rTRzJkz1bx5c0l3B42cMmWKZs2apa1btyp9+vSODDfNsRiedcJjFhISogoVKihXrlyaOnWqSpUqpR9++EEvvfSSduzYoYoVK973tfEDOACPIjo6Wj/99JMaNmwoV1dX642cq1evqmnTpipWrJjGjx8vDw8P60lo7969mj59uoYPH65s2bI5+BMgLYtvLTh+/Hiigfribdy4UePGjdOJEyeUMWNGVa5cWW+++SYJtwPF77fg4GD5+fnJ3d3dZvm1a9e0e/duffLJJwoNDZWnp6eqVKmibt260fcejyRhsnzvYGnx10VXr17V3LlztXLlSp0+fVqFCxdWxYoV9eGHH9JggSTFp4AWiyVRvVq/fr3Gjh2rNWvWKDAwUNHR0cqYMaPOnj2rpUuXqkyZMg6KOu0i6cZjFX/QDwkJUWBgoIoXL66GDRvqgw8+0Oeff6727dtbTy73HgCA1LBnzx6VL1/eWr9mz56thQsXasWKFfLy8tK8efPUoUMHjR49Wp06dVLGjBkVFhZmHXxm2bJltBTgka1YsUKDBg3S3LlzVb58eZtl8XUzJiZGFovF5rE+ONaSJUs0ZswYzZkzR8WLF09yHWOMbt68KQ8PDzk5OXG8QKqZNWuWihQpomrVqtkk4vdeN125ckVZs2a1vo4GC0j/qycJ60NwcLBy585tXSfhueb8+fPasWOHNm3apKioKNWoUUNBQUHKmzevQ+JP60i68dglTLwrVqyof/75Rx9++KGGDBkiKfFdXCC1xE9rsWrVKjVs2FAxMTGaMWOGZs2apYCAAM2dO1fp06fXxIkT1bdvX9WsWVNxcXG6deuWIiIitHfvXrm6uvKIHlIk/th2/vx5de7cWS+99JK6du36r+vf+/94vOK/+7CwML388stq2rSpevTo8cB1gdQUP1NG/vz5Vbt2bc2ePfu+68WPYH6/lnH8tx09elTffvuthgwZosWLF6tLly7av3+/8uXLZ10nqannqEOPjqtG2FXCezrx/x//mJOfn59+++035cuXTxs3btShQ4ckMSo57Kd27drq2rWr2rRpo1WrVsnFxUVvvPGG3n77bQUHB6tt27a6efOmevbsqR9//FE1a9ZU4cKF1bRpU+3bt8/6KDoJN1IifgCaoUOHKiYmRvXq1ZOU9BRT8esn9f94vCwWi9atW6cuXbrIx8dHDRs2fOC6QGqLn9Lys88+0969e/Xbb78luV58/UtqfmVAkv744w99+OGHatasmVq1aqWJEyfaJNxS4jqT8OYvUo6WbtjNv7UGJmzxLleunPLnz68JEybQTwR2derUKY0aNUqLFi3SvHnz1LhxY0VFRWnhwoWaPn26cuTIoa+++krp06dPVId5RA+PaunSpWrbtq1iYmL0ww8/6LnnnnN0SHgIGzduVMOGDRUbG6udO3eqbNmyjg4JT7H7tSwePXpUzZo1U5cuXfTOO+/w1BVS5J133tHkyZPVoEEDLV++nL7+jwm/VNjFr7/+ap1n9t1337XOX5pQwhbvvXv3avv27ZoxY8bjDhX/MQEBAerXr59atmypdu3aacWKFfLw8NBrr72mrl276sKFC2rfvr0iIiISXcyQcONRNWvWTMuXL1eWLFk0e/Zs6xM+eLLVrl1ba9eulaenp0aPHq2IiAhHh4SnVMKE+7vvvtOcOXOs5UWKFFHr1q01atQoBQcHk3AjWe7cuSPpbsv1G2+8oR9//FEffPCBrl69muT6tMumLlq6kari4uJ07do1+fr66tVXX1WGDBm0ZMkSbdu2TSVLlkzyNfEt3tevX1eGDBlIbJCq7tc6ferUKY0cOVLfffedTYv3okWLNHz4cLVq1Uoff/yxAyLG0yL+4vnGjRu6ffu2zcBGy5YtU8+ePdWgQQP16tVLxYoVc2CkSCjh6PLBwcHy9PRU7ty5lStXLq1fv15NmjRR8+bNNWnSJKVLl87R4eIptWvXLn322Wf66aefVKdOHdWqVUvdunXT5cuX1b59e7Vr105t27altRv/Kv6YFh0dbZ3DXZJ14Nj+/furf//+ypw5s6S7U6eWKFHCUeE+tXieAKnKyclJWbJk0enTp1W0aFEZY7R8+fL7JtzS3Rbv2NhYZcyYURKP8CJ1xdelBQsWKCQkRNHR0Wrbtq3y5cun4cOHS5LatWun+fPn66WXXlKLFi3k6+urF154wZFhI42Lv8hZtWqVxo0bp9OnT6tcuXJq3bq1GjVqpKZNmyouLk69e/eWs7OzunXrplKlSjk67P+8+P22bNky9e3bV+nSpVP69OkVFRWladOmqW7dulq5cqUaN24sZ2dnTZgwQV5eXo4OG0+Bn3/+WeHh4WrRooV69+6trFmzaubMmbryf+3dd3iN9//H8efJQkISMVPEbAhqRYlNzVLEjr1LzVKjsWcQe0dozBArYpSYDaJmrNAEsWdiNXue8/n94ZfzTUq/35ZwhPfjunpdcp/7Pn2fuN3nft2f9ewZ06dPZ/PmzSxatIjx48fz5MkTtm3bRrdu3SRwi/8q9Zp26NAhfHx8MDMzo3Xr1tSuXZvu3bsD0LNnTwB69OjB9u3bWbRoEdeuXcPKykrmBMhISogMotVqlVJKJScnq+DgYGVtba2yZs2qOnXqpG7duqXfT6fTvfHPQmSUTp06qVatWul/Hj58uLK0tFS1atVSuXPnVg4ODsrT01MlJiaqR48eqf79+ysbGxu1adOmdO+TkpLyoUsXmdSbrmW7d+9W2bNnVxMmTFABAQGqXr16qnLlymrZsmUqOTlZKaXU9u3blbm5ufrxxx9VYmLihy5bvMGJEyeUpaWlWrZsmVJKKV9fX6XRaNS0adP0+xw5ckRpNBo1cOBAQ5UpPiEvX75UPXr0UMWLF1ctW7ZUWbJkUZcuXdK/npSUpKKjo9Xo0aOVi4uLypMnj9JoNK99ZwnxJocOHVLGxsaqW7duqkiRIsrJyUlNmzZNxcfHK6WUWrduncqePbuqUKGCyp07tzp37pyBK/40SegWGSI1cCulVFBQkP7PoaGhytzcXLVr107dvn3bAJWJz41Op1M+Pj7KyspK9e7dW7148ULVqlVLnTt3Th90unXrpipXrqw2btyolFIqLCxMubi4qEaNGunfQ4h/4+HDh0qp/1wLb9++rRwdHdXChQuVUkrFxsaqAgUKqKJFi6ry5curFStW6M/HnTt3quvXrxumcPGaefPmqa5duyqllLp//76ys7NLF66fPn2qlFLq6NGjKiQkxCA1ik9H6jUjNDRUlSxZUmk0GjV79myl1Kvvor8+/L1//746ePCgcnBwUD169Pjg9YrM5f79+2rUqFFq6dKlSiml4uLi1KBBg5STk5OaPHmyPnifOXNG+fv7q7t37xqy3E+a9EkR7yzteKJx48YxaNAgvL29iY2NpWTJkgQGBvLrr7/i6upKWFgYAK1bt2bFihWGLFt8ojQaDe3atWPdunVs3ryZNm3aYG5uTtGiRfUzdP7yyy8ULFiQuXPnAlC8eHHmzZvHvn379O8hxD/l5+dHwYIFOXnyJEZGRuh0OrJmzUrnzp1xcXHh8ePHlCtXDmdnZy5duoROp2PJkiUsWLCAlJQUWrRowZdffmnojyH+X3R0NFmzZuXu3btUq1aNJk2asHjxYgD8/f1ZvXo1sbGx1K5dm1KlShm4WpGZqf9fCgzg2bNnNGzYEGdnZ9asWYOPjw8ajQZjY2O0Wq3+mIIFC9KgQQOWLVuGj48Ply5dMlT54iN38eJFevbsyd69eylevDgA2bJlw83Nja+//pp9+/Yxd+5cEhIS+Prrr2ncuDF2dnYGrvrTJaFbvLPUL4yxY8eyYsUKpkyZQrNmzbCwsEApRcWKFTl69Cj79u2jS5culC9fnpCQEHr16mXgysWnJvXGxMjIiObNm+Pt7c2tW7c4f/48SUlJACQlJWFiYoK7uztXrlwhMDAQAFtbW31gEuLfKFq0KG3atKFNmzacOnUKIyMjbGxscHFxIW/evLi7u1O5cmXc3NzIkSMH1apV4/Hjxxw/fpzo6GhDly/+In/+/Jw4cYLq1avTtGlTVqxYgUajQavV4ufnx+3bt2UcrXhnOp1O/4B35MiRtG3bllGjRjF58mScnJyYPHkymzdvBv4zN8mDBw/0x5YvX55SpUrJNUT8LVtbW8zNzbl16xbHjh3Tb8+RIwczZszAycmJDRs2sGTJEgNW+fmQbw2RIS5evIivry9+fn40aNAAgJCQEBYvXsy5c+eoXLkygYGBNG7cmLZt2xIcHIypqSkpKSkGrlx8SlJvTG7duoVGo+G7775j8eLFJCcn89NPPwHoZ+6MiYkhf/785MiRI917yM20+Kf8/f0BKF++PJMmTaJOnTq0aNGCkydPYmZmRv78+QH0M2BbWloCkCVLFubOnYuHhwc5c+Y0WP2fM6WU/vsnNDSUy5cvExwcDMD3339PoUKFiIiIoGfPnsTGxhIdHc348ePx8/NjyJAhZMuWzZDli09A6nfN06dPSUpKYv369RQqVIivvvqKIUOGULNmTaZOncrGjRsBaN68uX75MCMjIzZu3MilS5ekZVLoqb8sSJUvXz5WrVpFixYtOHDgAL/88ov+NQsLC6ZOnUrLli1p27bthy71syRLhokMERYWRqNGjVi4cCGFCxfGw8ODw4cPo5QiLCyMU6dOUaVKlXRd0VOXChMiI+3cuZNWrVqxZ88emjZtqt/WtWtXGjVqRK9evciZMyfTpk0jIiKCU6dOyWz54l8LDQ2ldOnS9OnTB09PTwCuXLnC9OnTOXz4MLt27cLJyYn4+HgGDBjA3bt3adCgAeHh4axbt47Lly9TqFAhA3+Kz8+cOXMwMzNjyJAhwKt1kAcPHkyWLFkwMzOjT58+/Pzzz8TGxlK3bl0iIiIwMTGhaNGihISEsGfPHipWrGjgTyE+FRs2bKBbt26UKlUKHx8fypUrp38tODgYDw8P1q1bR+HChUlKSuLq1auYmpoCcO7cObJkySKrHgjgP7OU//7775w4cYKnT59Sv359GjduzPPnz/nhhx94/PgxPXr0oHfv3q8dJz4Ag40mF5lW2knTUt29e1e1adNGOTg4qKxZs6oBAwaobdu2qcjISFWpUiXl7u5ugErF56pDhw4qd+7cau/evfptfn5+Kl++fEqj0ajhw4er7t27q6SkJKWUzFIu/r2UlBS1efNmZWlpqfr166ffHhwcrJ9d+Pfff1dKvZpUrWnTpqpKlSqqUqVK6sKFCwaq+vMWExOjfvzxR5U1a1a1cuVKpZRSpUqVUqtXr1ZHjhxRM2bMUKampmrcuHH6YzZs2KDmz5+vtm7dKhMMiQx37do15ezsrExMTNTRo0eVUko/waJSSj148EAdOHBALVmyRL897etCpLV9+3aVO3du1axZM9WtWzel0Wj017Pw8HDVtm1bVbduXbVkyRIDV/p5ktAt/pW0gfvw4cNq06ZN6t69e0oppe7du6f8/f3VsWPH9PslJCSor7/+Wq1evdoQ5YpP3F8fAKWddbxTp07K2tpaH7x1Op3as2eP0mg0avr06fr95AZGvK2UlBS1detWZW5u/rfB+/jx40oppSIjI1V0dLT6888/DVWuUEo9evRITZgwQeXIkUONGTNGDRw4UL9UW3R0tFqyZIkyNjZWY8aMMXCl4lPzpgYLpV41WtStW1cVKFBA/2Dn776X5AGx+DshISGqUKFCasWKFUqpV9czExMT5erqqj9vnjx5oho2bKi+/fZb+S4yAAnd4q2MGDFC2djYqHz58qk8efKo+fPnq4iICP3rcXFxKiwsTDVt2lQ5OjpKsBHv1eLFi/VL1aUN3h07dlQ5c+ZU+/btUzqdTul0OnXs2DE5H0WGSU5OVlu3blXZsmV7Y/C2tbVVAQEBBqzw85Z6PUj996/UqzWRXV1dVc6cOVXFihXT7Z8avLNmzapGjBjxwesVn6a0gXvz5s1q+vTpasaMGeq3335TSr16GFS7dm1lZ2enb8iQgC3+zs6dO1VUVFS6badOnVJ169ZVSr1aBrVAgQLq+++/179+8+ZNpdSrFu/79+9/uGKFnoRu8Y+kvfgfO3ZMVatWTR0/flxFRkaqkSNHqlKlSqkpU6aox48fK6WUWrZsmfr2229VzZo1pQuvyHDPnj1T9+/fV1qtViUmJqpq1aopW1tbdfnyZaXUf260tVqtcnR0VCVLllR+fn7p3kOCt3gbb1rDXafTqS1btrwWvK9cuaKaNm2qSpQooeLj42X99w8s9ff97Nkz/XfT9u3b1ZUrV9Tz58/VxIkTlUajUcuWLUt3XExMjJo9e7bKlSuXevr0qfy9iQwzcuRIZWtrq7p27aoaNWqkihcvrubMmaOUUurWrVuqXr16qmjRourWrVsGrlR8rO7fv680Go1ycXFR0dHR+u379u1TxYoVU2fPnlVFixZV33//vf5hz2+//aaaN2+uf6AjDENCt/ivgoOD0/28evVqNXjwYDVkyJB028eOHascHBzU1KlTVXR0tLp165batGmTPmhLwBEZZdOmTcrJyUnlzZtX1ahRQ/3666/q+fPnqkmTJqpw4cLq0qVL+n2TkpJUhw4dVK5cuVSjRo0MWLX4FKSGr2PHjqnZs2erAQMGqGPHjqknT54opV61YP01eP/xxx/qwYMHBqlXvArcX331lZo9e7ZauXKl0mg0asOGDUoppR4/fqzGjBmjsmfPrjw8PNIdFxsbq168eGGIksUnytfXVxUqVEidOnVKKfXqfipr1qzK29tbv8+9e/dU6dKlVatWrQxVpsgEjh49qmxsbFTnzp31wfv58+eqadOmytzcXHXq1Ekp9Z/vrJ9//ll988036unTpwarWUjoFv9Fnz591KhRo5RS//mH26pVK6XRaFTt2rVVTExMuv3HjRunypQpo0aOHJnu6Zu0cIuMsmLFCmVubq4mTpyoFixYoEqXLq2KFCmiQkNDVWxsrKpbt64qUqSIunjxokpISFBKKdWlSxd17dq1vx1PJ8S/sW3bNmVubq6aNm2qypYtqwoWLKh69eqlQkJClFJKbdmyRVlZWanOnTsbuFKRavr06fpJFBcvXpzutUePHqmxY8eqHDly6CdXE+J9mDNnjmrRooVS6tV1JEeOHGr58uVKqVfDGlInWHz8+LHcN4n/6fjx48rS0lJ17txZxcbGKqVe9TItVaqU6tWrlwoNDVVBQUFq1KhRytraWt8TUBiOhG7xt44dO6bvGp521tZBgwYpW1tbtWzZMhUZGZnumMGDB6uOHTtKdzyR4VatWqVMTU2Vr6+vftvz58+VsbGxmjhxolLq1Y1Lo0aNVM6cOZWzs7P6+uuvVdmyZfU3MBK8xbu4efOmKlGihPL09NRf41auXKnq16+v+vTpo54+faq0Wq3asGGD+uKLL/RdmoVhpPawCgkJUebm5ipnzpxq7ty56tGjR+n2S51cTaPRyKSfIsOlfu8sWLBADRkyRO3fv19lz55dH7iVetVLZtKkSenuqSR4i//l2LFjytLSUnXs2FF/vZs1a5aqVauWMjIyUuXLl1cVK1aUFTM+ErJOt/ifvLy82LZtG6NHj6ZOnToA9OjRg5MnTzJixAhcXFzIkSOHfn/1/2v+KVn7T2QApRQpKSnY29uTmJjI6dOn+eKLL9BoNBgZGVGpUiWcnZ0ZN26cfg348ePH8/TpU4yMjFi0aBEmJibp1ogX4r/5u3Pl6tWrNGrUiK1bt1K9enX99pUrVzJp0iT9Gs5arZa4uLh010Xx/qX+vcXHx2NkZER4eDi2trYA3Llzh61bt7J8+XJ++OEHevbsqX8NICoqivnz59OhQwdKlSplqI8gPgF/d/3Yv38/3377LQBr1qyhW7duAMTGxtK6dWvs7e1ZvHjxB61VZB5/d0997NgxvvvuO5o2bYq3tzfGxsZERUURHBxMwYIFyZ49O7ly5TJAxeKv5A5U/E/W1taEh4fj6enJsWPHgFdfGE5OTsydO5ctW7YQFRWl318Ct8hIGo0GU1NTTp8+jZmZGR07duSPP/7AyMiIPXv2cPHiRRo3boyRkRFarRaAqVOn4uHhwbJlyzAxMSElJUUCt/hHUm+Y7969y/Lly5k+fTrh4eEA+vMoPj4egKSkJAD69u1LlixZ2LFjBwDGxsYSuD+w1L+3kJAQunTpQuXKlSlevDiVKlVi8uTJfPnll4wZM4bevXuzfPly1q1bx+PHjwGYMWMGDx48YOLEiRK4xTtJG7h37tyJl5cXW7duJTo6msaNGzN//nyMjIyIi4vj/PnzBAUF0bp1ayIiIpg/fz7wKlwJkVbqPfVvv/3G+PHj6dKlC76+voSFhVG7dm327NnDvn376NKlC7GxsVhaWlKjRg0KFy4sgfsjIi3dIp2/C8t79uxh6tSpFC1alAEDBlC7dm0AevbsyY4dO1i/fj3Nmzf/0OWKz0RKSgomJiZERETg6OhI6dKladasGePGjWPRokX06NFDf7MjD3zE20o9h4KDg2nTpg3Vq1fHwsKC+fPnY2ZmBkCTJk24e/cugYGB+puZhIQEGjRoQM+ePendu7chP8JnKfXffHBwMLVq1aJLly5UrFgRGxsb1q5di7+/P/Xq1WPv3r1oNBomTJjAhg0bqF27NsbGxqxevZqgoCAqVqxo6I8iMrG03z3Dhg1j/fr1WFpaotFoSExMZPfu3Xz11VfMmDGDefPmYWZmRsGCBcmVKxe//vorpqamaLVajI2NDfxJxMfI19eXzp074+zszK1bt4iNjaVAgQK4ubnh6OjI8ePHadWqFdWrV2fjxo1kz57d0CWLvzJAl3bxkUo73vXmzZvq8uXL6dYB9PPzU1WqVFEdOnRQR48e1W+fOnWqjD0S713qeKXw8HBVpEgRpdFo1OTJk/WvyzwCIiNcu3ZN5c6dW40ZMybdqgupf378+LFydHRU9vb2aseOHWr//v1qzJgxysbGRt24ccNQZX/2IiIiVMWKFdXPP//82vYlS5YoCwsL1aZNG/32BQsWqPbt26sGDRrIBEPinaX9/jl37pyqWbOmOnfunHr+/Lm6du2acnZ2Vnnz5lXXr19XSr1a1SAoKEj98ccf+nsvWeVF/J379++rsmXLqiVLlui3+fn5qVatWqkmTZrol5g7cuSIKly4sHr48KGhShX/hbR0CyB9l6jx48ezZ88eQkNDadiwIfXr12fo0KHAq+5SM2fOpGjRovTs2ZOGDRvq30Oe0IqMotK0GKT9c2qL9/Pnz/n666+xs7Nj6dKllClTxpDlik+AUork5GT69++PVqvFw8ODbNmy6V9L23vi6dOn9O3bl+DgYHQ6HdbW1nh5eUlLqQFduHCBbt26sWnTJhwcHDA2NtZ/r0VGRrJkyRLc3NxYt24dbdq0AdAPE0j9exbiXW3evJnVq1djamrK9u3b9T1kYmNjadWqFX/++SfHjh0ja9as6Y6TOUdEKjc3N2xtbenZs6d+W1hYGLVq1WLdunXp7rt37NjBmDFjWLx4MQ0aNABeXdfkmvZxkn/hAkB/sZ8yZQqenp5Mnz6d0NBQtFotixcvZtKkSQC0bNkSV1dXTp06RWBgYLr3kMAtMoJOp0sXcNL+OXV8dq5cuTh16hS3bt1i4MCBXLx40QCVik+JRqPBxMSE06dP4+DgkO6mJfUcTJ0zIE+ePPj5+XHgwAF+++03Dh06JIHbwC5dukRYWBhly5bF2NgYpZT+e83KyopOnTphamrK7du39cdky5ZNbk5FhklKSiIwMJCQkBCuX7+uD9wpKSlYWFjQp08fXrx4QURExGvHSuAWOp2OZ8+eERkZiZOTU7rXjIyMyJUrl34eCp1OB0CrVq3QaDTs3r1bv69c0z5e8q9c6J07d46dO3eyceNGmjZtyu3bt/ntt98oUqQIW7Zswc3NDYAWLVqwbt06JkyYYOCKxafm999/5+HDhwD89NNPzJs377V9UoN33rx5CQoK4sSJE3h6en7oUsUnRqfT8eDBA+7fv4+DgwPw6mY5rdQHi7NnzyYmJobixYtTpEgRmajmI1CiRAkAtm/fDvDavA5FixalWLFi+uuLEO8qNfikMjMzY9q0afTs2ZM///yTIUOGkJiYiImJCQC2trYkJycTGxtriHLFRyz1IaGFhQUTJ07EwcGBgIAAVqxYAUCxYsUoWbIkkyZN4sqVK/qHNFqtlkKFClGkSBEDVi/+KQndn7G/fmHY29vTr18/nJycOHLkCO3bt2fx4sX4+/uTLVs2PDw8GDZsGAA1a9bE2NhY3/IjxLvQ6XQ8f/6cmjVrMmLECPr06cMvv/xCo0aN3rh/avDOkycPT58+lWVWxDszMjKiYMGClCpViuXLlxMZGYmJiclrMwmfPXsWX19fnj17ZqBKxZsUKVIES0tL1q1bx927d/XbU7/nXr58SbZs2XB0dDRUieITkrY7+I0bN7h37x53797FysqKESNG0LdvXwIDA/n++++5ffs258+fZ/r06RQsWJCSJUsauHrxsdFoNLx8+ZJChQqxbds24NUDxClTpuiD9/bt28mXLx/Nmzdn6dKlbN++HVdXV06fPk2zZs0MWb74hyR0f8ZSvzDu3bsHQI4cOejSpQsWFhZ4eXnRvXt3unfvjomJCeXKlSNXrlwopdLdhEqXcpERUrtO3blzh927d+Pt7Y2Pjw9ly5b922NMTEzQarVYW1vLAyCRIYyMjGjYsCGnT59mxYoVREdHv9ZiumfPHnLlyoWNjY2BqhRvUrBgQZYvX46/vz/jx4/n6tWrwH++5+bNm8ejR4+oVauWIcsUn4C0QxfGjRtHixYtqFmzJjVq1GDhwoVkz54dV1dXvvvuO/z8/HB0dMTNzY0vvviCQ4cOYWRk9FqjhxAWFhbUqlWLPXv2oNVqGTBgAB06dGD+/PksW7YMgJMnT1KtWjVWr17NiBEjOH78OAEBAdjb2xu4evFPmBi6APHhpX1Ce+TIERo0aMCJEyeoVq0a5ubmADx8+BBzc3N9S09CQgKjR4+mQ4cOsg63yFCp52NKSgpRUVFkyZKFhIQE1q9fT8mSJSlatCjw5snV0j70kQdA4l2knlPTp0/n1KlTTJo0iRcvXjB06FBsbW0JCwtjyZIlbNiwgYCAACwtLQ1dsviLli1bsmjRIgYNGsSZM2eoUaMGtra23L59m3379nH48GEKFy5s6DJFJpf6PTRr1iw8PDzYsGEDOp2OK1euMGzYMB49esSsWbMYOXIkSin2799PwYIFcXd3x8zMjISEhNcmUhPCzMyM+vXrM2HCBO7evYuDgwN9+/ZFq9WyaNEiAAYMGMDGjRt58uQJOp0Oc3NzrK2tDVu4+Mdk9vLPTNrAvXLlSp49e8bYsWPJlSsXO3bsoGbNmsTFxeHq6kpQUBBFixblwYMHPH/+nAsXLqSbEVaId5X2XDp//jyVKlUC4Nq1a1SqVIlmzZrh7u4u45XEe/fX1Rfat2/P0aNHSUpKIn/+/FhYWBAXF8fGjRupUKGC4QoV/9Pp06dxd3fn2rVrWFtbU758eQYPHkypUqUMXZrIxFK/r1JXOmjevDl16tRhzJgx+n22bNmCi4sLmzZtokOHDkRFReHm5kZAQAANGjRg/PjxZMmSxYCfQnws/m6VlkqVKmFvb4+Pjw8A169fZ+nSpezfv59hw4bRr18/g9Us3o0kp89MasD5+eefmThxInny5GHy5Ml89dVXNG7cmICAAMzNzfnxxx+pUqUKUVFRFChQgKCgIAncIkOlPZfGjRvHoEGD8Pb2JjY2lpIlSxIYGMivv/6Kq6srYWFhALRu3Vo/vkmIjJKSkoKxsTF3796lZs2aXLp0iS1btrB27Vp+/vlnWrZsyZgxYzh48KAE7kygatWqbNmyheDgYAIDA1m8eLEEbvFO0nYpDwkJwczMjBs3bqSb0ColJYX27dvTvXt3NmzYQHx8PJaWlowdO5aGDRuybds2Zs2aZciPIT4CqUMLkpKS9Ns0Go1+4s6OHTsSFhbGjRs3gFfzLQ0cOJCmTZsyfvx41q5d++GLFhlCWro/Q/fv36dBgwZMnjwZFxcX4NW4bldXV/z8/PD396dWrVokJiameyKbukayEBlp7NixeHp6smnTJipXroy1tbX+qe+5c+do0KABpUqVIj4+nqSkJC5fvoypqamhyxafmBs3blC/fn2+/fZblixZIudYJvd3rUhC/Ftpz58RI0bg6+tLWFgYw4cP58CBA/j5+WFvb6/vLfPjjz9y69Ytdu3apT82KiqKhQsX0rVrV+m5Jbh9+zbDhw+nRYsWuLi4pFvm68GDB5QrV44hQ4bol+uFVw971q1bR58+fShevLgBqhbvSposP0MJCQncu3cv3TiQQoUKMXHiRGxtbXF2dubUqVNkyZJF/0ROKSWBW2S4ixcv4uvri5+fHw0aNABefbEsXryYc+fOUblyZQIDA2ncuDFt27YlODgYU1PT15ZyEuKfSH3GfOnSJfbu3YuPj49+puuff/6ZGjVq4OHhoQ/caZ9Jy/PpzCVtyJbALd5W2sB98eJFbt68yfr16zEyMqJVq1YULFiQ0aNHc/PmTYyNjUlMTOTq1avY2toCr849nU6HpaUl48aNk8AtgFf34SkpKXz//fc0adKEMWPGEB0dTWJiIgULFmTUqFFs376da9eu6Y9xcHBgypQpErgzMWnp/sT93dP+xo0bkzt3bpYuXaoP3zqdjjZt2hASEsLDhw85e/asdMkT71VYWBiNGjVi4cKFFC5cGA8PDw4fPoxSirCwME6dOkWVKlXSdUWXHhfiXfj6+tK/f3/Kly/PtWvXKF68OL169aJz585oNBoJaEKI13h7e+Pp6YmJiQm7du3CwsICAB8fH7y8vDhz5gyVKlXixYsXpKSkcOHCBUxNTaWXhfivLl++zNKlSzl8+DDJycn64QmJiYm0atWK5cuX06xZs9fmHBGZk7R0f8J0Ol26i33aJZW+/fZbbt26xfz580lISABePXnTaDTMnj2bKlWqMG/ePJKTk6WFR2SINy2RYmZmRqVKlRg9ejRVq1ZFo9Hg5ubGuXPnqFixIkePHgVIN4+ABG7xtoKCgvjhhx+YPn06Bw8eZMuWLRw9epSHDx9iZGSU7gGlEOLzdf36dU6fPs25c+cAiIyM5PHjx1y9epX4+Hj9fi4uLixdupQ5c+ZQtWpVunfvzsWLF/U9siRwi/+mXLlyLFq0iHPnztG+fXtOnjxJpUqV2L17N/Hx8YwfP56YmBgJ3J8Iaen+RKVtGVy2bBmBgYFER0fTrFkz+vfvj1KKsWPHcvDgQQBq167N8ePHUUpx9uxZXFxciI+PZ+fOnYb8GOIT8ddl6iIiIqhRowaFChXi/v37/PHHH5ibm1OjRg2MjIxITEykVq1aDBgwgB49ehi2eJHppbY2eXl54e3tzeHDh7l58yYNGzakQYMGeHp6AnDnzh3p/inEZ27t2rXMmjWLhw8fkj17dtq3b8/8+fPZtGkTEyZMoFKlSixYsEDfhfxNpGVSvI1nz56xZ88e1qxZw9mzZ8mSJQvXrl0jT548hi5NZABpMvpEpZ2lfMOGDbRt25bSpUszYMAAHj16xJQpU3Bzc8PJyYl9+/YREhJCxYoV9WsBGhkZYWdnp5/VV57WineRej6OHDkSLy8vTE1N0el0jBkzhs6dO9O4cWMA4uPjefToEUOGDEGn09GlSxdDli0yoTcNRYiKisLKyoqEhAQKFy5MfHw8devWpWnTpixfvhyAffv28ccff9C3b19Zg1uIz9SKFSsYOnQoCxcupHjx4vj5+bFp0ybs7OwYNmwYz549Y/PmzYwZM4YZM2aQP3/+NwZsCdzi30h9MJw7d2569OhB06ZNuXPnDrlz55bA/SlR4pPl4+OjihUrpk6dOqWUUurAgQPKyMhIaTQa9cMPPyitVqvfNyUlRSmlVFxcnBo1apSysbFRISEhBqlbfDpSzyullDp27JiqVq2aOn78uIqMjFQjR45UpUqVUlOmTFGPHz9WSim1bNky9e2336qaNWuqpKSk195DiH/ixo0b6vfff1dKKbV161Y1YMAAlZycrAICApRGo1Hm5uZqxIgRSqfT6Y/p37+/at++vYqOjjZU2UIIA9qxY4fSaDRq165d+m2RkZGqQoUKqmXLlvptixYtUjVr1lS9evVSDx48MEClQojMSMZ0f6JSUlKIjY1l+PDhVK1alV9//ZV27drpl2by8PBg8uTJxMTEAK+eyt65c4cff/yRPXv2cOjQIZlETby1K1euAP952r9mzRq2bt3K119/Tc2aNbG0tMTd3Z02bdqwadMmVq1aRUxMDE2aNKFbt24EBATox8RJi4H4N+Lj45k9ezY1a9Zk+vTptG/fnqpVq2JiYkKdOnWYNm0aWq2WSpUqkZSUxKNHj3B1dWXr1q1MnDiR7NmzG/ojCCE+sMTERPbv30+xYsX0KxoAWFpa8tVXX6HRaIiLiwNg8ODBdOjQgcDAQNasWWOgioUQmY6hU794fyIiItStW7fUkydPVIUKFdTs2bOVUkqFhoYqGxsbpdFo1Ny5c9Mdc+HCBXlyK95Jnz591KhRo5RSSt+S2KpVK6XRaFTt2rVVTExMuv3HjRunypQpo0aOHJmulVFauMW/sXnzZv2fHz9+rKpVq6aMjIzUuHHjlFJKJScnK6WUun37tho2bJjSaDTqyy+/VBUrVlQlSpRQ58+fN0jdQoiPw6NHj9TQoUNVlSpVlJubm1JKqb179yqNRqMOHz6slEr/vbR161b5nhJC/GMykdpn4NKlS7i4uLB582bKlSvHvXv3cHd3p127dtSoUQMTExNZ1kJkmOPHj+Pk5ISpqSn37t3Dzs4OeNU6sH37dsaPH0/nzp3TjZsdMmQIz549w9vbW85D8a8FBwfTsGFDzpw5g52dHdHR0Xz33XdER0fz4MEDfH19qVmzZrrr3KlTp7hx4wZ58uThq6++okCBAgb+FEIIQ3vy5AnTp0/nwoULFC5cmN27d7N48WK6d++uny8i7bwRIJOmCSH+GQndn4GQkBDKlCnDzJkzqVu3LpMnTyYlJQV/f380Go2seyzeCy8vL7Zt28bo0aOpU6cOAD169ODkyZOMGDECFxcXcuTIod8/NRDJAyDxb+l0OqKjo7GysuLKlSuULVuWmJgYnj59yrhx4zhw4AC+vr7UqlVLf4McFxeHubm5oUsXQnxkHj9+zIwZM9iyZQtOTk74+fkBEq6FEO9GxnRnYmnXPU5KSnrjPkopHBwcWLRoET///DNdunTh6dOn7NmzRx9wJHCL98Ha2prw8HA8PT05duwY8Gpst5OTE3PnzmXLli1ERUXp95fALd6WkZERVlZWhIeHU7lyZbp160b27NkpWrQoY8aMoVGjRrRp04bAwECMjY2ZOXMmrq6uJCQkyJrcQoh0bG1tGTt2LO3btyc8PJxZs2YBr+YokeuFEOJtSUt3JpW2e9PKlSsxNTWladOm5M2b92+PuXnzJrGxsZQtWxYjIyNp4RYZ5u/C8p49e5g6dSpFixZlwIAB1K5dG4CePXuyY8cO1q9fT/PmzT90ueITlZKSgq+vL/369aNt27asXLkSgD/++INZs2axfv16mjRpwoEDBzh79iwVK1Y0cMVCiI/VkydPcHNzIygoiHr16jFt2jRDlySEyMQkcWVSqYF71KhRrF27Fnd399eewKYNQkopihcvrn9Np9NJ4BYZIu0DoFu3bhEbG0uRIkXIkSMH3333HVqtFjc3N5YtWwZA7dq1Wb16NcWLF6dp06aGLF1kYjqdDo1Gg0aj0Z+DJiYmtGnTBhMTE7p27Qq8eihZunRp3N3dqVWrFteuXWP+/PmULFnSwJ9ACPExy58/P2PGjGHUqFFERERITywhxDuRlu5MbNmyZUybNo29e/dSoUIF4NWYo5iYGKysrIC/b4EUIiOkDdzjx49nz549hIaG0rBhQ+rXr8/QoUMB2LlzJzNnzqRo0aL07NmThg0b6t9DxsmJfyMsLIxChQqRJUsWAPbv38+vv/7Ky5cv+emnnyhbtiwmJibs2LGDrl274uLiwqpVq/THyzVRCPFvvHjxAmtra4yMjOT6IYR4azKmOxO7evUqzZs3p0KFCty6dQtvb29q1KhBly5d2LRpE4B8OYj3KjVwT5kyBU9PT6ZPn05oaCharZbFixczadIkAFq2bImrqyunTp0iMDAw3XtI4Bb/lK+vL/b29uzfvx+Aw4cP06JFCx49esTFixepU6cOa9euJSYmhlatWrF+/Xq2b9+Oi4uL/j3kmiiE+DdsbGz0s5bL9UMI8bakpTuT+OvT1ZSUFH744QeuX79OnTp1OHr0KDlz5sTGxgadTkdoaCh79uwhV65c8iUh3qtz587Rr18/3N3dqV+/PgEBATRt2pTq1avz6NEjunTpwpgxYwAIDAykWrVqErTFv5L2+ufs7MyJEydYt24dAQEBFCtWjH79+gEwdOhQNm7cyPTp0+ncuTMWFhb4+PgwYsQIzp49i62trSE/hhBCCCE+U9LSnQmkfbqanJxMdHQ0JiYmjB49mty5c7Njxw5atGjBpEmT8PLyol69elhYWGBhYSGBW2S4tLPmA9jb29OvXz+cnJw4cuQI7du3Z/Hixfj7+5MtWzY8PDwYNmwYADVr1sTY2BitVmuI0kUmpdFouHPnDgB+fn5Uq1aN7t27c+TIkXRBeuHChXTq1IkxY8awadMmoqOjcXFxITQ0VAK3EEIIIQxGZtL6yKUdMztz5kzOnj3L6dOn6dWrFy1btmT79u1ERUVhaWkJvGoB37p1K7ly5SJr1qyGLF18olLPx3v37mFnZ0eOHDno0qUL5ubmeHl50b17d7p3746JiQnlypXj4sWLKKXStVZKS7f4N+Lj46lfvz5OTk54e3uza9cuunfvzvr16wkLCyM5ORlTU1PgVfA2Njbm+++/x8TEhO7du5M9e3YDfwIhhBBCfM6kpfsjlxpwxo4dy7x58/juu+9wc3Njy5YtDBs2jMePH2NpaUlUVBSbNm2iZcuW3L17F29vb/26x0JkhLQt3EeOHKFIkSKcPHkSjUaDubk5AA8fPiQyMhITExOUUiQkJDB69Gjmz58v56N4a1mzZuWHH34gKCgIPz8/ANauXUv79u2ZOnUq+/fvJzk5Wb//vHnzGD16NNWrV5fePkIIIYQwOAndmUBwcDC7du3C19eXnj17Urx4cW7fvk3v3r2xtbVFKUVSUhJ79+7FxsaGCxcuYGpqSkpKitxwigzx13XhT58+DUCLFi30E6PFxcVRrlw5/vjjD7p27co333zD1atXadeunX5ZJzkfxT/x1yEMGo2GZs2aYW1tze7du4mKigLAx8eH+vXr071799eC94wZM7C3t/+gdQshhBBCvImE7kzAyMgIjUZDzZo12bZtG02aNGHhwoV0796duLg4duzYgbW1NUuWLGHdunWYmJig1WplHW6RYVID988//8zEiRPJkycPkydP5quvvqJx48YEBARgbm7Ojz/+SJUqVYiKiqJAgQIEBQVhbGycLrQL8b8YGRlx584dDhw4oN/m4ODA0KFDWb16NUePHtVv37ZtG/Xq1aNPnz7s2rUrXfAWQgghhPgYyOzlH5k3rQEZFBREmzZtGDlyJOPGjWPatGkMHDgQgOPHj7NgwQLGjx+vX6tb1pEU78P9+/dp0KABkydP1i/BdO/ePVxdXfHz88Pf359atWqRmJioX0MZXs0zIA+AxD+llCIqKoqCBQsSGxvLxIkT6dixI8WKFcPExIRu3brx+++/c/DgQYoWLao/rlGjRty8eZNLly7JGG4hhBBCfFSk6ekjkrb7bVxcnH67o6MjderUYfDgwYwcOVIfuOPj43F3d0er1VKuXDn9/hK4xfuQkJDAvXv3sLa21m8rVKgQEydOxNbWFmdnZ06dOkWWLFn03YOVUhK4xb+i0WiwsrJi6NChlC5dmq1bt+Lu7o6bmxtJSUmMHj2anDlzsm7dOhISEvQz4R84cICjR49K4BZCCCHER0dauj9C7u7uHDx4EDs7O5o3b46zszP37t2jb9++nDlzhgkTJhAdHc3x48d5/Pixfgy3dOEVGSVtb4m0f27cuDG5c+dm6dKl+vCt0+lo06YNISEhPHz4kLNnz1KqVClDlS4yobTXrtSZyH///Xd8fHwoX748ALt37+b27dv4+PjoV3LYu3cvRYoUea13hRBCCCHEx0QS2kdm8eLFzJ49m6pVq3Lx4kXmzJnDnDlzsLOzY+PGjXTr1o0NGzbw+++/U6pUKS5evKifNE0Ct8gIf53wLO2a2t9++y23bt1i/vz5JCQkAK9awDUaDbNnz6ZKlSrMmzeP5ORkmalc/GOpY7gfP36sX/ordeZxPz8/evfujZ+fH5UrV2bgwIEUKlSI0NBQRo8eDSCBWwghhBAfNWnpNrC/tk5PnDiRChUq0KpVKyIiIpg6dSqnT5+mbdu2jBo1CoAXL15gY2OjP0bGzIqMkvZ8XLZsGYGBgURHR9OsWTP69++PUoqxY8dy8OBBAGrXrs3x48dRSnH27FlcXFyIj49n586dhvwYIpOJjY2ldevWHDx4EA8PD+rVq8eXX34JQKlSpahbty4eHh4AbNq0iQsXLjBnzhyyZMnC/fv3yZ07tyHLF0IIIYT4r6Rp1ICUUvqAs3v3bvbu3cv169f1gTpv3ryMHTsWJycnfH19mTFjBkC6wC1jZkVGSjtLuZubG3nz5qVq1aoMGDCACRMmoNFocHNzY/z48VSuXJmQkBAqVqyoXzbMyMgIOzs7UlJSpKVb/GMWFhYsW7aMkSNHMnbsWIYOHcq6desAWLVqFQ8ePGDHjh0AdOzYkfHjx+Pv78+FCxckcAshhBDioyct3QaSdpzs8OHD8fLywtTUlD///JNu3brxyy+/6PcNDw9n5syZ7Nq1i0mTJtG1a1dDlS0+A5s3b2bMmDFs3LiRqlWrcvDgQZo0aYJSiv79+7NkyRJ9ONdqtRgbGxMfH8+kSZNYtWoVJ06ckDHd4h/762oLO3fuZPv27WzevJnevXvj7OzMtm3bKFCgAGPHjsXExERWaBBCCCFEpiJNpB9Y6s1i6g3jkydPCA4OJiAggCxZsrBlyxY2btzIqFGjcHd3ByBfvnyMHDmSwoUL06lTJ0OWLz5xKSkpxMbGMnz4cKpWrcqvv/5K586d8fT0JHv27HTs2JE8efIwcuRIsmfPjrGxMXfu3GHGjBkEBgZy6NAhCdziX0m9FqYObWjZsiU1atSgVatWDBw4kFu3bpGYmMjWrVupUqUK3377rQRuIYQQQmQq0tL9Ad27dw87Ozv9z4sWLcLb25tixYrh5eVFtmzZePbsGatWrWL9+vU0a9ZMH7zTSm1dFOJ9ePr0KTExMZibm9OkSRM6d+7MiBEjuHbtGtWrV+fly5fMmTOH4cOH64+5ePEiefLkoUCBAgasXGQGy5Yto2TJktSrV+9/Tv748OFD3N3dCQ0N5eDBg1StWpWjR49iamoqwVsIIYQQmYa0dH8gI0aMICgoiN9++w2lFElJSZiZmREREUFMTAzZsmUDIHfu3PTp0weNRoO3tzd//vknnp6e6d5LArd4n/LkyUOePHm4dOkSCQkJNGrUCIBs2bLRsWNH2rVrR40aNYD/9NyoUKGCASsWmcmyZcuIjIxk48aN1KhR42+Dt1arpUCBAri5uXH9+nWKFCnCoEGDMDMz+8AVCyGEEEK8G2np/kDCw8OxsbHB1NSUZ8+ekTt3bl6+fMnu3bsZNGgQ7dq1SzeO+/nz58yfP587d+6wfv16adURH1xISAhlypRh5syZ1K1bl8mTJ5OSkoK/vz8ajUZmzRf/Stpx2HXr1uXu3busWbOGGjVq/O15JGO3hRBCCPEpkND9gfn6+tK2bVuuXLlC6dKliYyMZMeOHYwePZqWLVuma9WOjIzE0tISjUYjN58iw6RdFiy1x8VfpZ5vS5YsYciQIZQoUQJra2tOnDiBqampnI/iraSebykpKXz99deYmZkxa9YsatWqJT14hBBCCPHJktD9nqUNJ4cPH8bGxoZx48Zx4cIFDh8+jIODgz54u7q64uzszPLly//2PYR4F2kD98qVKzE1NaVp06bkzZv3b4+5efMmsbGxlC1bFiMjI2nhFm8l9Tq2efNmdu/ezdOnTzl48CD29vZ4enpSs2bN/znGWwghhBAiM5I7nPcobVgeN24cP/30ExYWFqxYsYKKFStSp04dQkJCsLKyolWrVsycOZMVK1a8NnmaBG6RUVJDzahRoxg3bpy+F0VaaX9WSlG8eHHKlSuHkZEROp1OArd4KxqNhhMnTtCzZ0+++eYbZs+eTVBQEJaWlvTo0YPAwEC0Wq2hyxRCCCGEyHDS0v0B3L59m2HDhjF48GDq168PwIMHD+jXrx9nz57l6NGjODg48PLlS06ePEnjxo2lq6V4b5YtW8a0adPYu3evfgI0rVZLTEwMVlZWgPSuEO+Hh4cHK1euJDAwUD95pFarxcnJidjYWDw8PKhevbo82BFCCCHEJ0Vaut+zBQsWUK9ePZ48eUKxYsX02wsWLMiKFSuoUqUK33zzDcHBweTMmZOmTZtibGwsLT7ivbl69SrNmzenQoUK3Lp1C29vb2rUqEGXLl3YtGkTIL0rRMZKfbYbFRXF8+fP9YE7Pj4eY2Nj5s2bR2hoKD169ODMmTOGLFUIIYQQIsNJ6M5gly5dYufOnZw4cQIAFxcXTExMOHPmDKGhoen2TQ3ehQsXxtXVNd1r0tItMsJfO7KkpKSQlJREaGgoEyZMoGfPnmzdupXSpUuTK1cuFi5cyLNnz147Toh/K+05lPoQx8XFhZiYGEaNGgWgD9+mpqa0bduWIkWK/Nf5BYQQQgghMiPpw5eBvL29mTNnDnZ2dpQpUwYnJyfy58/PqVOncHR0ZPLkyfrXUhUoUIDdu3eTK1cuA1YuPkVpJ01LTk4mISGBHDlyMHr0aEaPHs2OHTvo0aMH9evXp0KFCqxdu5b79+9jYWEhLd3inaQOTwgKCuLChQvY29tTsmRJ7OzsGD9+PIsXL0an0zFnzhxevnzJnj17yJEjBxs3bpSu5UIIIYT45MiY7gyybt06+vfvj5eXF02aNMHa2hpAP9NzREQElSpVolixYnh4eFC6dOnX3iNtSBLiXaQ9l2bOnMnZs2c5ffo0vXr1omXLljg6OhIVFYWlpSXw6jx1dnbG3NyczZs3S+gW72zHjh107dqVwoULc//+fdq1a8ewYcOwt7dn5cqVTJ48GWNjY6ytrQkPD+fQoUNUqlTJ0GULIYQQQmQ4Cd0Z4OrVq3To0IEff/yRPn366LentvakBu/w8HAqV65MiRIlWLBgAeXLlzdg1eJzMHbsWFauXMmsWbMwNjbGzc2NvHnzsnnzZmxtbYmKiuLXX39lw4YN3Lt3j/Pnz8s63OKtpZ439+/fZ/jw4TRq1IhevXrh4+ODh4cHefPmZeLEiZQrV46IiAh27NiBlZUVVapUSTfnhRBCCCHEp0SaVTPAw4cPiYuLo3bt2m8cx5g6PjtfvnycOXOGY8eO4enpaZBaxecjODiYXbt24evrS8+ePSlevDi3b9+md+/e2NraopQiKSmJvXv3YmNjw4ULFzA1NSUlJUUCt3grGo2GM2fOMH36dBISEvjuu+8wNjamc+fODBs2jKdPnzJlyhROnTpF3rx56devHy4uLhK4hRBCCPFJk8FzGSAoKIjo6Gjs7e2B15db0mg0hISE8OTJE+rVq8fTp0/1SzMJ8b4YGRmh0WioWbMm27Zto2fPnixcuJDu3bsTFxeHv78/LVq0YMmSJVhaWqLRaNBqtTKmVryTM2fOsGvXLpKTkwkPD8fW1haA1q1bA7B06VImTpyIm5sbjo6OhixVCCGEEOKDkJbuDFCiRAliY2M5cOAA8OblltatW8emTZtITk7GxsZGlgUTGepNo0QSEhKIiopi6dKl9O3bl5kzZ9K/f3/g1YMib29vrly5gpWVFRqNBqWUzJov3tmgQYOYOXMmNjY2LFy4kGvXrulfa926NX379sXU1JR8+fIZsEohhBBCiA9HQncGcHR0xMzMDE9PT+7du6ffnnZt2hs3bvDVV19hamqqf10CjsgIOp1O/6AnLi5Ov93R0ZE6deowePBgRo4cycCBA4FXayO7u7uj1WopV66cfn/pUi7+rdRrXExMDH/++ad+e7du3Rg6dCiXL19mwYIFXL9+Xf+ai4sLPj4+FCxY8EOXK4QQQghhEDKRWgbx8fGhR48etGnThhEjRlCxYkUAHj16RJ8+fYiKiiIgIEC67or3xt3dnYMHD2JnZ0fz5s1xdnbm3r179O3blzNnzjBhwgSio6M5fvw4jx8/1o/hllnzxdtIHUazZ88eli1bxtWrV/nuu++oV68ebdu2BWDJkiV4eXlRo0YNBgwYgIODg4GrFkIIIYT48CR0ZxCtVsvq1asZMGAA+fLlo2zZsuh0OiIjI9HpdJw4cQJTU1O0Wq20cIsMt3jxYqZMmUK/fv3Yt28f2bJlw9nZmREjRvD8+XOmTJlCYGAgefLk4csvv2T+/PmYmJjoZ9YX4m3s3r0bFxcXRo4cSalSpVi/fj1Pnjyhf//+9O3bF4Dly5fj7u5OmzZtmDFjRrrePkIIIYQQnwMJ3Rns4sWLeHl5ce3aNQoVKkTFihXp378/xsbGEnBEhvlr6/TEiROpUKECrVq1IiIigqlTp3L69Gnatm3LqFGjAHjx4gU2Njb6Y+R8FO8iLCyMtm3b0q9fP3744Qfi4uIoVqwYNjY2ZMuWjQEDBtC7d28AVq5cSYMGDShatKiBqxZCCCGE+PAkdH8g0sItMkra2fF3796NsbEx69evp3///tSpUweAJ0+e4ObmxpkzZ2jZsiWurq5/+x5CvI3w8HCWLl3KwIEDSUlJoU6dOjRu3JihQ4fi7OwMQL9+/Rg6dKhhCxVCCCGEMDAJ3e+BBBrxvqQ9t4YPH46Xlxempqb8+eefdOvWjV9++UW/b3h4ODNnzmTXrl1MmjSJrl27Gqps8Yl6/vw5uXLlYtCgQbx48QIPDw8sLS3p06cPBw8epHz58qxdu5acOXMaulQhhBBCCIOR2ZPeAwncIqOlPhtLPbeePHlCcHAwAQEBHDt2jHHjxhEYGKjvSg6QL18+Ro4cyeDBg+nUqZNB6haZW0RExH99PVeuXADcuHEDS0tLLC0tATAzM2PYsGGsWrVKArcQQgghPnsyoFOIj9y9e/ews7PT/7xo0SK8vb0pVqwYJUuWJFu2bAwcOJAsWbKwfv164NVM5gBffPEFP/74IyBDHMS/s3HjRjw9PVmwYAEVKlR44z5KKZKSkihatCh37txh7ty5PH78mG3btnH+/Hny5s37YYsWQgghhPgISUu3EB+xESNG0L17d+BVwElMTMTMzIyIiAguX75MtmzZAMidOzd9+vShW7du+Pv78/3337/2XhK4xb+h1WoBmDJlChcvXnzjPhqNhixZstCzZ0/Mzc3x8vLi0KFD7N+/X9bhFkIIIYT4fzKmW4iPWHh4ODY2NpiamvLs2TNy587Ny5cv2b17N4MGDaJdu3bpxnE/f/6c+fPnc+fOHdavXy9DHcQ72bFjB6tWrcLExIRp06bx1Vdf/e2+L1++xMjICJ1OJ13KhRBCCCHSkNAtRCbg6+tL27ZtuXLlCqVLlyYyMpIdO3YwevRoWrZsiaenp37fyMhILC0t0Wg0MqmfeCupy8ndvXuXpUuXsmXLFsqVK8fMmTMpXbq0ocsTQgghhMhUpHu5EB+htM/CDh8+TNGiRfn2229p0KABISEhWFlZ0apVK2bNmsXu3bv54Ycf9PtbWVlJ4BbvxMTEhM2bN1OvXj3Cw8Oxs7Pj2LFjuLq6cunSJUOXJ4QQQgiRqUjoFuIjkzYsjxs3jp9++gkLCwtWrFhBxYoVqVOnTrrgPXPmTFasWKGfPC2VBG7xtu7cucPo0aP56aef8PLy4tixY8ybN48///yTCRMm8Mcffxi6RCGEEEKITENCtxAfmdSwfPv2ba5cucLcuXOxt7enYMGCrFixgq+//jpd8G7RogV79uzhp59+MnDl4lORlJREfHw8JUuW1E/A16tXL7p168aRI0cYN24cFy5cMHCVQgghhBCZg4RuIT5CCxYsoF69ejx58oRixYrpt6cG7ypVqvDNN98QHBxMzpw5adq0KcbGxvoZp4X4N/46tYexsTH58uXj4cOHAOh0OgB69+5NmTJlOHnyJHPmzCExMfGD1yqEEEIIkdlI6BbiI3Dp0iV27tzJiRMnAHBxccHExIQzZ84QGhqabt/U4F24cGFcXV3TvSbLgom3odFoOHr0KG5ubgAUL16cMmXKMH78eC5cuICR0auvivj4eIoVK8agQYOYPXs2WbJkMWTZQgghhBCZgsxeLoSBeXt7M2fOHOzs7ChTpgxTp07F2NiYZ8+e4ejoiK2tLb/88gtlypRJd9zTp0/JlSuXPhAJ8baSkpKYMWMG8+bN4+eff9Y/zKlTpw63bt3C1dWVL774ghMnTrBr1y6OHTtGvnz5DFy1EEIIIUTmIKFbCANat24d/fv3x8vLiyZNmmBtbQ38Z8mmiIgIKlWqRLFixfDw8Hjjck06nU6Ct3graSfte/jwIWvWrMHDw4N+/foxbtw4AHr06MGVK1cIDw/HysqKtWvX4ujoaMiyhRBCCCEyFQndQhjI1atX6dChAz/++CN9+vTRb08NQqnBOzw8nMqVK1OiRAkWLFhA+fLlDVi1+JQ8evSIL774It3Pq1atYuXKlfTv35+xY8cCrwK5TqfD3NycXLlyGapcIYQQQohMSZrHhDCQhw8fEhcXR+3atdNNZJXa8pg6PjtfvnycOXOGY8eO4enpaZBaxacnJCSEunXrpjunvvjiC3r37k2nTp2YO3cuS5YsAaBAgQIUKlRIArcQQgghxFuQ0C2EgQQFBREdHY29vT0ajea1GaQ1Gg0hISH89ttv2Nra8vTpUxYtWmSgasWnqFatWixevJg1a9botxUoUIDOnTuj0WgYMmQIc+fONVyBQgghhBCfAAndQhhIiRIliI2N5cCBA8B/WrjTWrduHZs2bSI5ORkbGxtZFkxkGAcHB4YPH06dOnWYOXMmXl5e+tfy5s1Lw4YNmTNnDs7OzoYrUgghhBDiE2Bi6AKE+Fw5OjpiZmaGp6cnpUqVws7ODvjPmO6oqChu3LhBnTp1MDU11R8ny4KJfyv1nAoNDSUiIoKUlBS++eYbypQpQ//+/dFoNMyYMYOoqCjatWvH0qVL+fPPP+nZsyc5c+Y0dPlCCCGEEJmaTKQmhAH5+PjQo0cP2rRpw4gRI6hYsSLwakKrPn36EBUVRUBAACYm8nxMvJ3UwL1jxw5GjRqlnxAtd+7cHDhwAFNTU0JCQti0aRMzZsygcOHCREdHs3//fipUqGDo8oUQQgghMj0J3UIYkFarZfXq1QwYMIB8+fJRtmxZdDodkZGR6HQ6Tpw4gampKVqtVlq4xVs7ePAgbdu2Zfbs2XTu3JnDhw/j7OyMk5MT+/fvJ0eOHCQlJXH79m0ePHiAg4NDulnNhRBCCCHE25PQLcRH4OLFi3h5eXHt2jUKFSpExYoV6d+/P8bGxvqlw4R4G7GxsQwaNIhSpUoxevRoHj16RPXq1alatSoXL17E0tKSI0eOkCNHDkOXKoQQQgjxSZLQLcRHTFq4xdtI7VIeFBREhQoV2L17N0WKFKFQoUI0aNAAJycnli5dypo1a+jTpw8ODg6cPn2a7NmzG7p0IYQQQohPjsxeLsRH4k3PvyRwi38rNXD7+/tTr149jh8/jrOzMxUqVOC3334je/bsjBkzBiMjI/LkyUPDhg3Jmzcv4eHhhi5dCCGEEOKTJKFbiI/Em5YME+J/0el06X7WaDQ8ePAAPz8/3NzcqFu3rv61mzdv6ocwAJw8eRJ7e3v8/f0pXrz4hyxbCCGEEOKzIaFbCCEyKZ1Oh5GREefPn2fq1KkAnD17lkGDBnHmzBkqVaoEoF/bvU2bNmTLlg17e3uaNWvG4sWL6devH1myZDHYZxBCCCGE+NRJ6BZCiEwoNXBfvnyZr7/+msjISABMTEx48uQJwcHBnDlzBng1TEEpRZEiRdi2bRv16tWjRIkSnDp1irJlyxryYwghhBBCfPJkIjUhhMhkUgP3pUuXqFatGsOGDWP69On68dy3b9+mb9++xMfHM3LkSJydnV97D5kVXwghhBDiw5CWbiGEyGSMjIwICwvDycmJn376KV3gXrt2LQkJCSxatIisWbOyYsUKdu3apT82tau5BG4hhBBCiA9DQrcQQmQyOp0OLy8vcuTIQa5cuYBXE6hNmzaNESNGEBkZSenSpZk3bx5JSUmsXLmSbdu2ATIjvhBCCCHEhybdy4UQIhN69OgR7u7unDp1ih49ehAVFcWcOXNYu3Yt3377rb7l+/Lly/Ts2ZNixYqxevVqWYtbCCGEEOIDk9AthBCZ1JMnT5g+fToHDx7k5s2b7N+/n2+++QatVqufPE2j0XDlyhVy5MhB4cKFDV2yEEIIIcRnRwb1CSFEJpU/f37GjRuHkZERAQEBXLhwgW+++QZjY+N0wVtmKBdCCCGEMBwJ3UIIkYnly5cPV1dXdDodW7duJSUlhdGjR2NsbKyf5VwIIYQQQhiOdC8XQohPQGpX8wsXLlC/fn0mT55s6JKEEEIIIQQye7kQQnwS8ufPz9ixY/nyyy/5/fffef78uaFLEkIIIYQQSEu3EEJ8UsLDw4FX3c6FEEIIIYThSegWQgghhBBCCCHeE+leLoQQQgghhBBCvCcSuoUQQgghhBBCiPdEQrcQQgghhBBCCPGeSOgWQgghhBBCCCHeEwndQgghhBBCCCHEeyKhWwghhBBCCCGEeE8kdAshhBBCCCGEEO+JhG4hhBDiIxAQEIBGo+HPP/80dClCCCGEyEASuoUQQogM9OTJEwYPHkyxYsXIkiULhQoVonnz5hw+fPi/Hle9enUeP36MlZXVB6o0vQsXLtCuXTvy5ctH1qxZ+fLLL+nbty/Xr1//x+/Ro0cPnJ2d31+RQgghRCYkoVsIIYTIIHfu3MHR0ZEjR44we/ZsgoOD8ff3p169egwcOPBvj0tOTsbMzIz8+fOj0Wg+YMWv7NmzBycnJxITE/H29iYkJIQNGzZgZWXF+PHjP3g9GUEpRUpKiqHLEEIIISR0CyGEEBllwIABaDQazpw5Q5s2bbC3t6dMmTIMHz6cU6dO6ffTaDQsX76cFi1aYGFhwfTp01/rXr5mzRqsra3Zs2cPJUuWxNzcnLZt2xIXF8fatWspUqQIOXPmZMiQIWi1Wv17JyYmMmLECAoUKICFhQVVq1YlICDgb2uOi4ujZ8+eNG3alF27dtGgQQOKFi1K1apVmTNnDitWrABAq9XSu3dvihYtSrZs2ShZsiQLFy7Uv8+kSZNYu3YtO3fuRKPRoNFo9P/f+/fv0759e6ytrbGxsaFly5bcuXNHf2xKSgpDhgzB2tqaXLlyMXr0aLp3756u1TwxMZEhQ4aQN29esmbNSs2aNTl79qz+9dTf3759+3B0dCRLlixs2LABIyMjzp07l+4zL1iwgMKFC6PT6f7pX60QQgjx1iR0CyGEEBngxYsX+Pv7M3DgQCwsLF573draOt3PkyZNolWrVgQHB9OrV683vmdcXByLFi3Cx8cHf39/AgICaNWqFXv37mXv3r2sX7+eFStWsG3bNv0xgwYN4uTJk/j4+HD58mXatWtHkyZNuHHjxhv/H/v37+fZs2eMGjXqja+n1q3T6ShYsCBbt27ljz/+YMKECYwZM4YtW7YAMGLECNq3b0+TJk14/Pgxjx8/pnr16iQnJ9O4cWNy5MjB8ePHOXHiBNmzZ6dJkyYkJSUBMGvWLLy9vVm9ejUnTpwgKioKPz+/dHWMGjWK7du3s3btWs6fP0+JEiVo3LgxL168SLffzz//zMyZMwkJCaFFixY0aNCA1atXp9tn9erV9OjRAyMjuQ0SQgjxASghhBBCvLPTp08rQPn6+v7PfQH1448/ptv222+/KUC9fPlSKaXU6tWrFaDCwsL0+/Tr10+Zm5ur6Oho/bbGjRurfv36KaWUunv3rjI2NlYPHz5M997169dXrq6ub6xl1qxZClAvXrz4R58zrYEDB6o2bdrof+7evbtq2bJlun3Wr1+vSpYsqXQ6nX5bYmKiypYtm9q/f79SSql8+fKp2bNn619PSUlRdnZ2+veKiYlRpqamytvbW79PUlKS+uKLL5S7u7tS6j+/Pz8/v3T//82bN6ucOXOqhIQEpZRSQUFBSqPRqNu3b//rzyuEEEK8DXnEK4QQQmQApdS/2r9y5cr/cx9zc3OKFy+u/zlfvnwUKVKE7Nmzp9sWEREBQHBwMFqtFnt7e7Jnz67/7+jRo9y8efOd6166dCmOjo7kyZOH7Nmz4+npyb179/7rMZcuXSIsLIwcOXLo67GxsSEhIYGbN28SGRlJeHg4VapU0R9jbGyMo6Oj/uebN2+SnJxMjRo19NtMTU2pUqUKISEh6f5/f/29Ojs7Y2xszI4dO4BX3fbr1atHkSJF/vHnFkIIId6FiaELEEIIIT4FX375JRqNhtDQ0H+0/5u6oP+Vqalpup81Gs0bt6WOTY6JicHY2JigoCCMjY3T7Zc2qKdlb28PQGhoKNWqVfvbWnx8fBgxYgRz586lWrVq5MiRg9mzZ3P69On/+hliYmJwdHTE29v7tdfy5MnzX499G3/9vZqZmdGtWzdWr15N69at2bhxY7qx6EIIIcT7Ji3dQgghRAawsbGhcePGLF26lNjY2Nde/xDrb1esWBGtVktERAQlSpRI91/+/PnfeEyjRo3InTs37u7ub3w9te4TJ05QvXp1BgwYQMWKFSlRosRrredmZmbpJnUDqFSpEjdu3CBv3ryv1WRlZYWVlRX58uVLNymaVqvl/Pnz+p+LFy+OmZkZJ06c0G9LTk7m7NmzlC5d+n/+Xvr06cOhQ4dYtmwZKSkptG7d+n8eI4QQQmQUCd1CCCFEBlm6dClarZYqVaqwfft2bty4QUhICIsWLfqvrcgZxd7ens6dO9OtWzd8fX25ffs2Z86cYcaMGfz6669vPMbCwoJVq1bx66+/0qJFCw4dOsSdO3c4d+4co0aNon///sCrlvxz586xf/9+rl+/zvjx49MFZYAiRYpw+fJlrl27xrNnz0hOTqZz587kzp2bli1bcvz4cW7fvk1AQABDhgzhwYMHAAwePJgZM2awc+dOrl27xtChQ3n58qV++TQLCwt++OEHRo4cib+/P3/88Qd9+/YlLi6O3r17/8/fi4ODA05OTowePZqOHTuSLVu2d/k1CyGEEP+KhG4hhBAigxQrVozz589Tr149fvrpJ8qWLUvDhg05fPgwy5cv/yA1rF69mm7duvHTTz9RsmRJnJ2dOXv2LHZ2dn97TMuWLfn9998xNTWlU6dOlCpVio4dOxIZGcm0adMA6NevH61bt6ZDhw5UrVqV58+fM2DAgHTv07dvX0qWLEnlypXJkycPJ06cwNzcnGPHjmFnZ0fr1q1xcHCgd+/eJCQkYGlpCaAPw926daNatWpkz56dxo0bkzVrVv17z5w5kzZt2tC1a1cqVapEWFgY+/fvJ2fOnP/o99K7d2+SkpL+dqZ4IYQQ4n3RqH8784sQQgghxHuk0+lwcHCgffv2TJ06NUPec+rUqWzdupXLly9nyPsJIYQQ/5RMpCaEEEIIg7p79y4HDhygTp06JCYmsmTJEm7fvk2nTp3e+b1jYmK4c+cOS5Ys0bfaCyGEEB+SdC8XQgghhEEZGRmxZs0avv76a2rUqEFwcDCHDh3CwcHhnd970KBBODo6UrduXelaLoQQwiCke7kQQgghhBBCCPGeSEu3EEIIIYQQQgjxnkjoFkIIIYQQQggh3hMJ3UIIIYQQQgghxHsioVsIIYQQQgghhHhPJHQLIYQQQgghhBDviYRuIYQQQgghhBDiPZHQLYQQQgghhBBCvCcSuoUQQgghhBBCiPdEQrcQQgghhBBCCPGe/B9yOjZPVacY6QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = data.groupby(['Crime_Category', 'Occurred_Weekend?']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Occurred_Weekend? and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Weekend?', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "category_counts = data.groupby(['Crime_Category', 'Occurred_Weekmonth']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Occurred_Weekmonth and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Weekmonth', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "category_counts = data.groupby(['Crime_Category', 'Occurred_Month']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Occurred_Month and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Month', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "category_counts = data.groupby(['Crime_Category', 'Occurred_Weekday']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Occurred_Weekday and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Weekday', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "category_counts = data.groupby(['Crime_Category', 'Occurred_Hour']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Occurred_Hour and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Hour', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# add more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = data.groupby(['Crime_Category', 'Part 1-2']).size().unstack(fill_value=0)\n",
    "category_percentage = category_counts.div(category_counts.sum(axis=1), axis=0) * 100\n",
    "category_percentage.plot(kind='bar', stacked=False, figsize=(10, 6), colormap='viridis')\n",
    "plt.title('Relation between Part and Crime_Category (in Percentage)')\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Part', loc='upper right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like Part 1-2 is ordinal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000,)\n",
      "(20000, 481)\n"
     ]
    },
    {
     "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>Street_Number</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Street_Name</th>\n",
       "      <th>Street_Type</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Crime_Category</th>\n",
       "      <th>Days_After_Reported</th>\n",
       "      <th>Reported_Day</th>\n",
       "      <th>Reported_Month</th>\n",
       "      <th>Reported_Weekday</th>\n",
       "      <th>Occurred_Day</th>\n",
       "      <th>Occurred_Month</th>\n",
       "      <th>Occurred_Weekday</th>\n",
       "      <th>Occurred_Weekmonth</th>\n",
       "      <th>Occurred_Weekend?</th>\n",
       "      <th>Occurred_Hour</th>\n",
       "      <th>0100</th>\n",
       "      <th>0104</th>\n",
       "      <th>0105</th>\n",
       "      <th>0110</th>\n",
       "      <th>0112</th>\n",
       "      <th>0113</th>\n",
       "      <th>0114</th>\n",
       "      <th>0115</th>\n",
       "      <th>0117</th>\n",
       "      <th>0120</th>\n",
       "      <th>0121</th>\n",
       "      <th>0200</th>\n",
       "      <th>0201</th>\n",
       "      <th>0202</th>\n",
       "      <th>0206</th>\n",
       "      <th>0207</th>\n",
       "      <th>0209</th>\n",
       "      <th>0210</th>\n",
       "      <th>0211</th>\n",
       "      <th>0212</th>\n",
       "      <th>0213</th>\n",
       "      <th>0216</th>\n",
       "      <th>0217</th>\n",
       "      <th>0220</th>\n",
       "      <th>0301</th>\n",
       "      <th>0302</th>\n",
       "      <th>0303</th>\n",
       "      <th>0304</th>\n",
       "      <th>0305</th>\n",
       "      <th>0307</th>\n",
       "      <th>0308</th>\n",
       "      <th>0309</th>\n",
       "      <th>0310</th>\n",
       "      <th>0311</th>\n",
       "      <th>0312</th>\n",
       "      <th>0314</th>\n",
       "      <th>0315</th>\n",
       "      <th>0316</th>\n",
       "      <th>0319</th>\n",
       "      <th>0321</th>\n",
       "      <th>0322</th>\n",
       "      <th>0323</th>\n",
       "      <th>0324</th>\n",
       "      <th>0325</th>\n",
       "      <th>0326</th>\n",
       "      <th>0327</th>\n",
       "      <th>0328</th>\n",
       "      <th>0329</th>\n",
       "      <th>0330</th>\n",
       "      <th>0332</th>\n",
       "      <th>0334</th>\n",
       "      <th>0335</th>\n",
       "      <th>0336</th>\n",
       "      <th>0337</th>\n",
       "      <th>0338</th>\n",
       "      <th>0339</th>\n",
       "      <th>0340</th>\n",
       "      <th>0341</th>\n",
       "      <th>0342</th>\n",
       "      <th>0343</th>\n",
       "      <th>0344</th>\n",
       "      <th>0345</th>\n",
       "      <th>0346</th>\n",
       "      <th>0347</th>\n",
       "      <th>0349</th>\n",
       "      <th>0350</th>\n",
       "      <th>0351</th>\n",
       "      <th>0352</th>\n",
       "      <th>0353</th>\n",
       "      <th>0354</th>\n",
       "      <th>0355</th>\n",
       "      <th>0356</th>\n",
       "      <th>0357</th>\n",
       "      <th>0358</th>\n",
       "      <th>0359</th>\n",
       "      <th>0360</th>\n",
       "      <th>0361</th>\n",
       "      <th>0362</th>\n",
       "      <th>0363</th>\n",
       "      <th>0364</th>\n",
       "      <th>0365</th>\n",
       "      <th>0366</th>\n",
       "      <th>0368</th>\n",
       "      <th>0369</th>\n",
       "      <th>0370</th>\n",
       "      <th>0371</th>\n",
       "      <th>0372</th>\n",
       "      <th>0373</th>\n",
       "      <th>0374</th>\n",
       "      <th>0375</th>\n",
       "      <th>0377</th>\n",
       "      <th>0378</th>\n",
       "      <th>0379</th>\n",
       "      <th>0380</th>\n",
       "      <th>0381</th>\n",
       "      <th>0382</th>\n",
       "      <th>0383</th>\n",
       "      <th>0384</th>\n",
       "      <th>0385</th>\n",
       "      <th>0386</th>\n",
       "      <th>0387</th>\n",
       "      <th>0388</th>\n",
       "      <th>0389</th>\n",
       "      <th>0390</th>\n",
       "      <th>0391</th>\n",
       "      <th>0392</th>\n",
       "      <th>0394</th>\n",
       "      <th>0395</th>\n",
       "      <th>0396</th>\n",
       "      <th>0397</th>\n",
       "      <th>0398</th>\n",
       "      <th>0399</th>\n",
       "      <th>0400</th>\n",
       "      <th>0401</th>\n",
       "      <th>0402</th>\n",
       "      <th>0404</th>\n",
       "      <th>0405</th>\n",
       "      <th>0406</th>\n",
       "      <th>0407</th>\n",
       "      <th>0408</th>\n",
       "      <th>0409</th>\n",
       "      <th>0410</th>\n",
       "      <th>0411</th>\n",
       "      <th>0413</th>\n",
       "      <th>0414</th>\n",
       "      <th>0416</th>\n",
       "      <th>0417</th>\n",
       "      <th>0418</th>\n",
       "      <th>0419</th>\n",
       "      <th>0420</th>\n",
       "      <th>0421</th>\n",
       "      <th>0422</th>\n",
       "      <th>0424</th>\n",
       "      <th>0425</th>\n",
       "      <th>0426</th>\n",
       "      <th>0429</th>\n",
       "      <th>0430</th>\n",
       "      <th>0431</th>\n",
       "      <th>0432</th>\n",
       "      <th>0433</th>\n",
       "      <th>0434</th>\n",
       "      <th>0437</th>\n",
       "      <th>0440</th>\n",
       "      <th>0442</th>\n",
       "      <th>0443</th>\n",
       "      <th>0444</th>\n",
       "      <th>0445</th>\n",
       "      <th>0446</th>\n",
       "      <th>0447</th>\n",
       "      <th>0448</th>\n",
       "      <th>0449</th>\n",
       "      <th>0450</th>\n",
       "      <th>0451</th>\n",
       "      <th>0500</th>\n",
       "      <th>0501</th>\n",
       "      <th>0503</th>\n",
       "      <th>0505</th>\n",
       "      <th>0507</th>\n",
       "      <th>0508</th>\n",
       "      <th>0509</th>\n",
       "      <th>0510</th>\n",
       "      <th>0512</th>\n",
       "      <th>0513</th>\n",
       "      <th>0514</th>\n",
       "      <th>0515</th>\n",
       "      <th>0516</th>\n",
       "      <th>0519</th>\n",
       "      <th>0520</th>\n",
       "      <th>0522</th>\n",
       "      <th>0525</th>\n",
       "      <th>0526</th>\n",
       "      <th>0527</th>\n",
       "      <th>0528</th>\n",
       "      <th>0529</th>\n",
       "      <th>0531</th>\n",
       "      <th>0532</th>\n",
       "      <th>0533</th>\n",
       "      <th>0534</th>\n",
       "      <th>0536</th>\n",
       "      <th>0537</th>\n",
       "      <th>0538</th>\n",
       "      <th>0541</th>\n",
       "      <th>0542</th>\n",
       "      <th>0543</th>\n",
       "      <th>0544</th>\n",
       "      <th>0545</th>\n",
       "      <th>0547</th>\n",
       "      <th>0549</th>\n",
       "      <th>0550</th>\n",
       "      <th>0551</th>\n",
       "      <th>0552</th>\n",
       "      <th>0553</th>\n",
       "      <th>0554</th>\n",
       "      <th>0555</th>\n",
       "      <th>0556</th>\n",
       "      <th>0557</th>\n",
       "      <th>0558</th>\n",
       "      <th>0559</th>\n",
       "      <th>0561</th>\n",
       "      <th>0562</th>\n",
       "      <th>0601</th>\n",
       "      <th>0602</th>\n",
       "      <th>0603</th>\n",
       "      <th>0604</th>\n",
       "      <th>0605</th>\n",
       "      <th>0701</th>\n",
       "      <th>0800</th>\n",
       "      <th>0850</th>\n",
       "      <th>0851</th>\n",
       "      <th>0853</th>\n",
       "      <th>0855</th>\n",
       "      <th>0856</th>\n",
       "      <th>0857</th>\n",
       "      <th>0858</th>\n",
       "      <th>0859</th>\n",
       "      <th>0902</th>\n",
       "      <th>0903</th>\n",
       "      <th>0906</th>\n",
       "      <th>0907</th>\n",
       "      <th>0908</th>\n",
       "      <th>0910</th>\n",
       "      <th>0911</th>\n",
       "      <th>0912</th>\n",
       "      <th>0913</th>\n",
       "      <th>0914</th>\n",
       "      <th>0915</th>\n",
       "      <th>0916</th>\n",
       "      <th>0917</th>\n",
       "      <th>0919</th>\n",
       "      <th>0922</th>\n",
       "      <th>0923</th>\n",
       "      <th>0924</th>\n",
       "      <th>0925</th>\n",
       "      <th>0926</th>\n",
       "      <th>0927</th>\n",
       "      <th>0928</th>\n",
       "      <th>0929</th>\n",
       "      <th>0930</th>\n",
       "      <th>0931</th>\n",
       "      <th>0933</th>\n",
       "      <th>0935</th>\n",
       "      <th>0938</th>\n",
       "      <th>0940</th>\n",
       "      <th>0942</th>\n",
       "      <th>0944</th>\n",
       "      <th>0945</th>\n",
       "      <th>0946</th>\n",
       "      <th>0947</th>\n",
       "      <th>1000</th>\n",
       "      <th>1006</th>\n",
       "      <th>1008</th>\n",
       "      <th>1009</th>\n",
       "      <th>1012</th>\n",
       "      <th>1013</th>\n",
       "      <th>1017</th>\n",
       "      <th>1018</th>\n",
       "      <th>1019</th>\n",
       "      <th>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1026</th>\n",
       "      <th>1028</th>\n",
       "      <th>1100</th>\n",
       "      <th>1202</th>\n",
       "      <th>1203</th>\n",
       "      <th>1204</th>\n",
       "      <th>1205</th>\n",
       "      <th>1206</th>\n",
       "      <th>1210</th>\n",
       "      <th>1211</th>\n",
       "      <th>1212</th>\n",
       "      <th>1213</th>\n",
       "      <th>1214</th>\n",
       "      <th>1215</th>\n",
       "      <th>1216</th>\n",
       "      <th>1218</th>\n",
       "      <th>1219</th>\n",
       "      <th>1221</th>\n",
       "      <th>1222</th>\n",
       "      <th>1223</th>\n",
       "      <th>1224</th>\n",
       "      <th>1225</th>\n",
       "      <th>1227</th>\n",
       "      <th>1229</th>\n",
       "      <th>1230</th>\n",
       "      <th>1232</th>\n",
       "      <th>1233</th>\n",
       "      <th>1234</th>\n",
       "      <th>1236</th>\n",
       "      <th>1237</th>\n",
       "      <th>1238</th>\n",
       "      <th>1239</th>\n",
       "      <th>1240</th>\n",
       "      <th>1241</th>\n",
       "      <th>1242</th>\n",
       "      <th>1243</th>\n",
       "      <th>1245</th>\n",
       "      <th>1248</th>\n",
       "      <th>1251</th>\n",
       "      <th>1252</th>\n",
       "      <th>1253</th>\n",
       "      <th>1254</th>\n",
       "      <th>1256</th>\n",
       "      <th>1257</th>\n",
       "      <th>1258</th>\n",
       "      <th>1259</th>\n",
       "      <th>1260</th>\n",
       "      <th>1261</th>\n",
       "      <th>1262</th>\n",
       "      <th>1265</th>\n",
       "      <th>1266</th>\n",
       "      <th>1267</th>\n",
       "      <th>1268</th>\n",
       "      <th>1270</th>\n",
       "      <th>1271</th>\n",
       "      <th>1273</th>\n",
       "      <th>1274</th>\n",
       "      <th>1275</th>\n",
       "      <th>1276</th>\n",
       "      <th>1277</th>\n",
       "      <th>1278</th>\n",
       "      <th>1279</th>\n",
       "      <th>1281</th>\n",
       "      <th>1300</th>\n",
       "      <th>1301</th>\n",
       "      <th>1302</th>\n",
       "      <th>1303</th>\n",
       "      <th>1304</th>\n",
       "      <th>1305</th>\n",
       "      <th>1306</th>\n",
       "      <th>1307</th>\n",
       "      <th>1308</th>\n",
       "      <th>1309</th>\n",
       "      <th>1310</th>\n",
       "      <th>1311</th>\n",
       "      <th>1312</th>\n",
       "      <th>1313</th>\n",
       "      <th>1315</th>\n",
       "      <th>1316</th>\n",
       "      <th>1317</th>\n",
       "      <th>1318</th>\n",
       "      <th>1401</th>\n",
       "      <th>1402</th>\n",
       "      <th>1403</th>\n",
       "      <th>1404</th>\n",
       "      <th>1405</th>\n",
       "      <th>1406</th>\n",
       "      <th>1407</th>\n",
       "      <th>1408</th>\n",
       "      <th>1409</th>\n",
       "      <th>1410</th>\n",
       "      <th>1411</th>\n",
       "      <th>1413</th>\n",
       "      <th>1414</th>\n",
       "      <th>1415</th>\n",
       "      <th>1416</th>\n",
       "      <th>1419</th>\n",
       "      <th>1420</th>\n",
       "      <th>1501</th>\n",
       "      <th>1510</th>\n",
       "      <th>1512</th>\n",
       "      <th>1513</th>\n",
       "      <th>1514</th>\n",
       "      <th>1516</th>\n",
       "      <th>1517</th>\n",
       "      <th>1519</th>\n",
       "      <th>1520</th>\n",
       "      <th>1522</th>\n",
       "      <th>1528</th>\n",
       "      <th>1531</th>\n",
       "      <th>1536</th>\n",
       "      <th>1538</th>\n",
       "      <th>1601</th>\n",
       "      <th>1602</th>\n",
       "      <th>1604</th>\n",
       "      <th>1605</th>\n",
       "      <th>1606</th>\n",
       "      <th>1607</th>\n",
       "      <th>1608</th>\n",
       "      <th>1609</th>\n",
       "      <th>1610</th>\n",
       "      <th>1611</th>\n",
       "      <th>1612</th>\n",
       "      <th>1701</th>\n",
       "      <th>1702</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1807</th>\n",
       "      <th>1809</th>\n",
       "      <th>1810</th>\n",
       "      <th>1811</th>\n",
       "      <th>1812</th>\n",
       "      <th>1813</th>\n",
       "      <th>1814</th>\n",
       "      <th>1815</th>\n",
       "      <th>1816</th>\n",
       "      <th>1817</th>\n",
       "      <th>1818</th>\n",
       "      <th>1819</th>\n",
       "      <th>1820</th>\n",
       "      <th>1821</th>\n",
       "      <th>1822</th>\n",
       "      <th>1823</th>\n",
       "      <th>1824</th>\n",
       "      <th>1900</th>\n",
       "      <th>1901</th>\n",
       "      <th>1902</th>\n",
       "      <th>1903</th>\n",
       "      <th>1906</th>\n",
       "      <th>1908</th>\n",
       "      <th>1910</th>\n",
       "      <th>1912</th>\n",
       "      <th>1913</th>\n",
       "      <th>1914</th>\n",
       "      <th>1915</th>\n",
       "      <th>1916</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2009</th>\n",
       "      <th>2011</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2040</th>\n",
       "      <th>2041</th>\n",
       "      <th>2042</th>\n",
       "      <th>2045</th>\n",
       "      <th>2046</th>\n",
       "      <th>2047</th>\n",
       "      <th>2048</th>\n",
       "      <th>2049</th>\n",
       "      <th>2050</th>\n",
       "      <th>2051</th>\n",
       "      <th>2052</th>\n",
       "      <th>2053</th>\n",
       "      <th>2054</th>\n",
       "      <th>2055</th>\n",
       "      <th>2056</th>\n",
       "      <th>2058</th>\n",
       "      <th>2059</th>\n",
       "      <th>2060</th>\n",
       "      <th>2104</th>\n",
       "      <th>2108</th>\n",
       "      <th>2126</th>\n",
       "      <th>2157</th>\n",
       "      <th>2201</th>\n",
       "      <th>2204</th>\n",
       "      <th>2303</th>\n",
       "      <th>2304</th>\n",
       "      <th>3003</th>\n",
       "      <th>3004</th>\n",
       "      <th>4025</th>\n",
       "      <th>9999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500</td>\n",
       "      <td>None</td>\n",
       "      <td>CARPENTER</td>\n",
       "      <td>AV</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>45TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>13:45:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>E</td>\n",
       "      <td>MARTIN LUTHER KING JR</td>\n",
       "      <td>BL</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>06:05:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900</td>\n",
       "      <td>None</td>\n",
       "      <td>ORO GRANDE</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>18</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100</td>\n",
       "      <td>S</td>\n",
       "      <td>VERMONT</td>\n",
       "      <td>AV</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Street_Number Direction            Street_Name Street_Type  Latitude  \\\n",
       "0          4500      None              CARPENTER          AV   34.1522   \n",
       "1          None      None                   45TH          ST   34.0028   \n",
       "2           600         E  MARTIN LUTHER KING JR          BL   34.0111   \n",
       "3         14900      None             ORO GRANDE          ST   34.2953   \n",
       "4          7100         S                VERMONT          AV   33.9787   \n",
       "\n",
       "   Longitude Date_Reported Date_Occurred Time_Occurred  Area_ID  \\\n",
       "0  -118.3910    2020-03-09    2020-03-06      18:00:00     15.0   \n",
       "1  -118.2391    2020-02-27    2020-02-27      13:45:00     13.0   \n",
       "2  -118.2653    2020-08-21    2020-08-21      06:05:00     13.0   \n",
       "3  -118.4590    2020-11-08    2020-11-06      18:00:00     19.0   \n",
       "4  -118.2918    2020-02-25    2020-02-25      11:30:00     12.0   \n",
       "\n",
       "   Reporting_District_no  Part 1-2  Victim_Age Victim_Sex Victim_Descent  \\\n",
       "0                 1563.0       1.0        75.0          M              W   \n",
       "1                 1367.0       1.0        41.0          M              H   \n",
       "2                 1343.0       2.0        67.0          M              B   \n",
       "3                 1924.0       1.0        61.0          M              H   \n",
       "4                 1245.0       1.0         0.0          X              X   \n",
       "\n",
       "   Premise_Code  Weapon_Used_Code Status   Crime_Category  \\\n",
       "0         101.0               NaN     IC  Property Crimes   \n",
       "1         216.0             400.0     IC  Property Crimes   \n",
       "2         501.0               NaN     IC  Property Crimes   \n",
       "3         101.0               NaN     IC  Property Crimes   \n",
       "4         401.0             400.0     IC  Property Crimes   \n",
       "\n",
       "   Days_After_Reported  Reported_Day  Reported_Month  Reported_Weekday  \\\n",
       "0                    3             9               3                 0   \n",
       "1                    0            27               2                 3   \n",
       "2                    0            21               8                 4   \n",
       "3                    2             8              11                 6   \n",
       "4                    0            25               2                 1   \n",
       "\n",
       "   Occurred_Day  Occurred_Month  Occurred_Weekday  Occurred_Weekmonth  \\\n",
       "0             6               3                 4                   1   \n",
       "1            27               2                 3                   4   \n",
       "2            21               8                 4                   3   \n",
       "3             6              11                 4                   1   \n",
       "4            25               2                 1                   4   \n",
       "\n",
       "   Occurred_Weekend?  Occurred_Hour  0100  0104  0105  0110  0112  0113  0114  \\\n",
       "0                  0             18   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1                  0             13   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2                  0              6   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3                  0             18   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4                  0             11   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0115  0117  0120  0121  0200  0201  0202  0206  0207  0209  0210  0211  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0212  0213  0216  0217  0220  0301  0302  0303  0304  0305  0307  0308  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0309  0310  0311  0312  0314  0315  0316  0319  0321  0322  0323  0324  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0325  0326  0327  0328      0329  0330  0332  0334  0335  0336  0337  0338  \\\n",
       "0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0  0.590094   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0  0.664081   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0339  0340  0341  0342  0343      0344  0345  0346  0347  0349  0350  0351  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.180678   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.227903   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "       0352  0353  0354  0355  0356  0357  0358  0359  0360  0361  0362  0363  \\\n",
       "0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  0.398643   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0364  0365  0366  0368  0369  0370      0371  0372  0373  0374  0375  0377  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0  0.509267   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0378  0379  0380  0381  0382  0383  0384  0385  0386  0387  0388  0389  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0390  0391  0392  0394  0395  0396  0397  0398  0399      0400  0401  0402  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.369837   0.0   0.0   \n",
       "\n",
       "   0404  0405  0406  0407  0408  0409  0410  0411  0413  0414      0416  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.240850   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.303802   \n",
       "\n",
       "       0417  0418  0419  0420  0421  0422  0424  0425  0426  0429  0430  0431  \\\n",
       "0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  0.412012   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4  0.519701   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0432  0433  0434  0437  0440  0442  0443  0444  0445      0446  0447  0448  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.370276   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "\n",
       "   0449  0450  0451  0500  0501  0503  0505  0507  0508  0509  0510  0512  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0513  0514  0515  0516  0519  0520  0522  0525  0526  0527  0528  0529  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0531  0532  0533  0534  0536  0537  0538  0541  0542  0543  0544  0545  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0547  0549  0550  0551  0552  0553  0554  0555  0556  0557  0558  0559  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0561  0562  0601  0602  0603  0604  0605  0701  0800  0850  0851  0853  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0855  0856  0857  0858  0859  0902  0903     0906  0907  0908  0910  0911  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.38948   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0912  0913  0914  0915  0916  0917  0919  0922  0923  0924  0925  0926  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0927  0928  0929  0930  0931  0933  0935  0938  0940  0942  0944      0945  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.630196   \n",
       "\n",
       "   0946  0947  1000  1006  1008  1009  1012  1013  1017  1018  1019  1020  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1021  1022  1026  1028  1100      1202  1203  1204  1205  1206  1210  1211  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.807334   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1212  1213  1214  1215  1216  1218  1219  1221  1222  1223  1224  1225  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1227  1229  1230  1232  1233  1234  1236  1237  1238  1239  1240  1241  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1242  1243  1245  1248  1251  1252  1253  1254  1256  1257  1258  1259  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1260  1261  1262  1265  1266  1267  1268  1270  1271  1273  1274  1275  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1276  1277  1278  1279  1281      1300  1301  1302  1303  1304  1305  1306  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.747661   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1307  1308  1309  1310  1311  1312  1313  1315  1316  1317  1318  1401  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1402  1403  1404  1405  1406  1407  1408  1409  1410  1411  1413  1414  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1415  1416  1419  1420  1501  1510  1512  1513  1514  1516  1517  1519  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1520  1522  1528  1531  1536  1538  1601  1602  1604  1605  1606  1607  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1608  1609  1610  1611  1612  1701  1702  1801  1802  1803  1804  1805  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1807  1809  1810  1811  1812  1813  1814  1815  1816  1817  1818  1819  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1820  1821      1822  1823  1824  1900  1901  1902  1903  1906  1908  1910  \\\n",
       "0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0  0.180347   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0  0.227485   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1912  1913  1914  1915  1916  2000  2001  2002  2003  2004  2005  2006  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2007  2009  2011  2013  2014  2018  2019  2020  2021  2022  2023  2024  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2025  2027  2028  2029  2030  2031  2032  2033  2034  2035  2036  2037  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2038  2040  2041  2042  2045  2046  2047  2048  2049  2050  2051  2052  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2053  2054  2055  2056  2058  2059  2060  2104  2108  2126  2157  2201  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2204  2303  2304  3003  3004  4025  9999  \n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "\n",
    "data['Modus_Operandi'] = data['Modus_Operandi'].fillna('')\n",
    "\n",
    "print(data['Modus_Operandi'].shape)\n",
    "\n",
    "vectorizer.fit_transform(data['Modus_Operandi'])\n",
    "modus = vectorizer.transform(data['Modus_Operandi'])\n",
    "feature_names = vectorizer.get_feature_names_out()\n",
    "\n",
    "print(modus.shape)\n",
    "\n",
    "modus_data = pd.DataFrame(data=modus.toarray(), columns=feature_names)\n",
    "data = pd.concat([data.drop('Modus_Operandi',axis=1),modus_data], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Number\n",
       "600      477\n",
       "700      414\n",
       "100      405\n",
       "800      405\n",
       "300      380\n",
       "        ... \n",
       "24400      1\n",
       "00000      1\n",
       "01200      1\n",
       "23500      1\n",
       "05900      1\n",
       "Name: count, Length: 267, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Street_Number'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Number_Encoded\n",
       "405.0    810\n",
       "477.0    477\n",
       "414.0    414\n",
       "97.0     388\n",
       "380.0    380\n",
       "        ... \n",
       "2.0       14\n",
       "6.0       12\n",
       "3.0       12\n",
       "4.0       12\n",
       "11.0      11\n",
       "Name: count, Length: 115, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "street_freq = data['Street_Number'].value_counts().to_dict()\n",
    "data['Street_Number_Encoded'] = data['Street_Number'].map(street_freq)\n",
    "data['Street_Number_Encoded'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Name\n",
       "FIGUEROA      274\n",
       "SEPULVEDA     226\n",
       "VERMONT       223\n",
       "VENTURA       205\n",
       "WILSHIRE      192\n",
       "             ... \n",
       "DUCOMMUN        1\n",
       "HOLLY MONT      1\n",
       "MEDINA          1\n",
       "MARIANO         1\n",
       "COLINA          1\n",
       "Name: count, Length: 2843, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Street_Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Name_Encoded\n",
       "1     1120\n",
       "2      972\n",
       "3      744\n",
       "4      744\n",
       "5      565\n",
       "      ... \n",
       "44      44\n",
       "43      43\n",
       "42      42\n",
       "41      41\n",
       "36      36\n",
       "Name: count, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "street_freq = data['Street_Name'].value_counts().to_dict()\n",
    "data['Street_Name_Encoded'] = data['Street_Name'].map(street_freq)\n",
    "data['Street_Name_Encoded'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Type\n",
       "ST    6581\n",
       "AV    5748\n",
       "BL    3724\n",
       "DR     776\n",
       "PL     563\n",
       "WY     307\n",
       "RD     247\n",
       "LN      69\n",
       "HY      65\n",
       "CT      41\n",
       "TR      37\n",
       "WK      21\n",
       "CI       8\n",
       "FY       3\n",
       "PK       1\n",
       "PY       1\n",
       "ML       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Street_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street_Type_Encoded\n",
       "6581.0    6581\n",
       "5748.0    5748\n",
       "3724.0    3724\n",
       "776.0      776\n",
       "563.0      563\n",
       "307.0      307\n",
       "247.0      247\n",
       "69.0        69\n",
       "65.0        65\n",
       "41.0        41\n",
       "37.0        37\n",
       "21.0        21\n",
       "8.0          8\n",
       "3.0          3\n",
       "1.0          3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "street_freq = data['Street_Type'].value_counts().to_dict()\n",
    "data['Street_Type_Encoded'] = data['Street_Type'].map(street_freq)\n",
    "data['Street_Type_Encoded'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1120\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reporting_District_no_Encoded\n",
       "23    828\n",
       "18    828\n",
       "16    752\n",
       "21    714\n",
       "20    680\n",
       "     ... \n",
       "56     56\n",
       "55     55\n",
       "52     52\n",
       "40     40\n",
       "1      29\n",
       "Name: count, Length: 67, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data['Reporting_District_no'].nunique())\n",
    "street_freq = data['Reporting_District_no'].value_counts().to_dict()\n",
    "data['Reporting_District_no_Encoded'] = data['Reporting_District_no'].map(street_freq)\n",
    "data['Reporting_District_no_Encoded'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "217\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Premise_Code_Encoded\n",
       "5033    5033\n",
       "3379    3379\n",
       "2491    2491\n",
       "1437    1437\n",
       "978      978\n",
       "        ... \n",
       "23        23\n",
       "11        22\n",
       "17        17\n",
       "14        14\n",
       "7          7\n",
       "Name: count, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data['Premise_Code'].nunique())\n",
    "street_freq = data['Premise_Code'].value_counts().to_dict()\n",
    "data['Premise_Code_Encoded'] = data['Premise_Code'].map(street_freq)\n",
    "data['Premise_Code_Encoded'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Street_Number</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Street_Name</th>\n",
       "      <th>Street_Type</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Status</th>\n",
       "      <th>Crime_Category</th>\n",
       "      <th>Days_After_Reported</th>\n",
       "      <th>Reported_Day</th>\n",
       "      <th>Reported_Month</th>\n",
       "      <th>Reported_Weekday</th>\n",
       "      <th>Occurred_Day</th>\n",
       "      <th>Occurred_Month</th>\n",
       "      <th>Occurred_Weekday</th>\n",
       "      <th>Occurred_Weekmonth</th>\n",
       "      <th>Occurred_Weekend?</th>\n",
       "      <th>Occurred_Hour</th>\n",
       "      <th>0100</th>\n",
       "      <th>0104</th>\n",
       "      <th>0105</th>\n",
       "      <th>0110</th>\n",
       "      <th>0112</th>\n",
       "      <th>0113</th>\n",
       "      <th>0114</th>\n",
       "      <th>0115</th>\n",
       "      <th>0117</th>\n",
       "      <th>0120</th>\n",
       "      <th>0121</th>\n",
       "      <th>0200</th>\n",
       "      <th>0201</th>\n",
       "      <th>0202</th>\n",
       "      <th>0206</th>\n",
       "      <th>0207</th>\n",
       "      <th>0209</th>\n",
       "      <th>0210</th>\n",
       "      <th>0211</th>\n",
       "      <th>0212</th>\n",
       "      <th>0213</th>\n",
       "      <th>0216</th>\n",
       "      <th>0217</th>\n",
       "      <th>0220</th>\n",
       "      <th>0301</th>\n",
       "      <th>0302</th>\n",
       "      <th>0303</th>\n",
       "      <th>0304</th>\n",
       "      <th>0305</th>\n",
       "      <th>0307</th>\n",
       "      <th>0308</th>\n",
       "      <th>0309</th>\n",
       "      <th>0310</th>\n",
       "      <th>0311</th>\n",
       "      <th>0312</th>\n",
       "      <th>0314</th>\n",
       "      <th>0315</th>\n",
       "      <th>0316</th>\n",
       "      <th>0319</th>\n",
       "      <th>0321</th>\n",
       "      <th>0322</th>\n",
       "      <th>0323</th>\n",
       "      <th>0324</th>\n",
       "      <th>0325</th>\n",
       "      <th>0326</th>\n",
       "      <th>0327</th>\n",
       "      <th>0328</th>\n",
       "      <th>0329</th>\n",
       "      <th>0330</th>\n",
       "      <th>0332</th>\n",
       "      <th>0334</th>\n",
       "      <th>0335</th>\n",
       "      <th>0336</th>\n",
       "      <th>0337</th>\n",
       "      <th>0338</th>\n",
       "      <th>0339</th>\n",
       "      <th>0340</th>\n",
       "      <th>0341</th>\n",
       "      <th>0342</th>\n",
       "      <th>0343</th>\n",
       "      <th>0344</th>\n",
       "      <th>0345</th>\n",
       "      <th>0346</th>\n",
       "      <th>0347</th>\n",
       "      <th>0349</th>\n",
       "      <th>0350</th>\n",
       "      <th>0351</th>\n",
       "      <th>0352</th>\n",
       "      <th>0353</th>\n",
       "      <th>0354</th>\n",
       "      <th>0355</th>\n",
       "      <th>0356</th>\n",
       "      <th>0357</th>\n",
       "      <th>0358</th>\n",
       "      <th>0359</th>\n",
       "      <th>0360</th>\n",
       "      <th>0361</th>\n",
       "      <th>0362</th>\n",
       "      <th>0363</th>\n",
       "      <th>0364</th>\n",
       "      <th>0365</th>\n",
       "      <th>0366</th>\n",
       "      <th>0368</th>\n",
       "      <th>0369</th>\n",
       "      <th>0370</th>\n",
       "      <th>0371</th>\n",
       "      <th>0372</th>\n",
       "      <th>0373</th>\n",
       "      <th>0374</th>\n",
       "      <th>0375</th>\n",
       "      <th>0377</th>\n",
       "      <th>0378</th>\n",
       "      <th>0379</th>\n",
       "      <th>0380</th>\n",
       "      <th>0381</th>\n",
       "      <th>0382</th>\n",
       "      <th>0383</th>\n",
       "      <th>0384</th>\n",
       "      <th>0385</th>\n",
       "      <th>0386</th>\n",
       "      <th>0387</th>\n",
       "      <th>0388</th>\n",
       "      <th>0389</th>\n",
       "      <th>0390</th>\n",
       "      <th>0391</th>\n",
       "      <th>0392</th>\n",
       "      <th>0394</th>\n",
       "      <th>0395</th>\n",
       "      <th>0396</th>\n",
       "      <th>0397</th>\n",
       "      <th>0398</th>\n",
       "      <th>0399</th>\n",
       "      <th>0400</th>\n",
       "      <th>0401</th>\n",
       "      <th>0402</th>\n",
       "      <th>0404</th>\n",
       "      <th>0405</th>\n",
       "      <th>0406</th>\n",
       "      <th>0407</th>\n",
       "      <th>0408</th>\n",
       "      <th>0409</th>\n",
       "      <th>0410</th>\n",
       "      <th>0411</th>\n",
       "      <th>0413</th>\n",
       "      <th>0414</th>\n",
       "      <th>0416</th>\n",
       "      <th>0417</th>\n",
       "      <th>0418</th>\n",
       "      <th>0419</th>\n",
       "      <th>0420</th>\n",
       "      <th>0421</th>\n",
       "      <th>0422</th>\n",
       "      <th>0424</th>\n",
       "      <th>0425</th>\n",
       "      <th>0426</th>\n",
       "      <th>0429</th>\n",
       "      <th>0430</th>\n",
       "      <th>0431</th>\n",
       "      <th>0432</th>\n",
       "      <th>0433</th>\n",
       "      <th>0434</th>\n",
       "      <th>0437</th>\n",
       "      <th>0440</th>\n",
       "      <th>0442</th>\n",
       "      <th>0443</th>\n",
       "      <th>0444</th>\n",
       "      <th>0445</th>\n",
       "      <th>0446</th>\n",
       "      <th>0447</th>\n",
       "      <th>0448</th>\n",
       "      <th>0449</th>\n",
       "      <th>0450</th>\n",
       "      <th>0451</th>\n",
       "      <th>0500</th>\n",
       "      <th>0501</th>\n",
       "      <th>0503</th>\n",
       "      <th>0505</th>\n",
       "      <th>0507</th>\n",
       "      <th>0508</th>\n",
       "      <th>0509</th>\n",
       "      <th>0510</th>\n",
       "      <th>0512</th>\n",
       "      <th>0513</th>\n",
       "      <th>0514</th>\n",
       "      <th>0515</th>\n",
       "      <th>0516</th>\n",
       "      <th>0519</th>\n",
       "      <th>0520</th>\n",
       "      <th>0522</th>\n",
       "      <th>0525</th>\n",
       "      <th>0526</th>\n",
       "      <th>0527</th>\n",
       "      <th>0528</th>\n",
       "      <th>0529</th>\n",
       "      <th>0531</th>\n",
       "      <th>0532</th>\n",
       "      <th>0533</th>\n",
       "      <th>0534</th>\n",
       "      <th>0536</th>\n",
       "      <th>0537</th>\n",
       "      <th>0538</th>\n",
       "      <th>0541</th>\n",
       "      <th>0542</th>\n",
       "      <th>0543</th>\n",
       "      <th>0544</th>\n",
       "      <th>0545</th>\n",
       "      <th>0547</th>\n",
       "      <th>0549</th>\n",
       "      <th>0550</th>\n",
       "      <th>0551</th>\n",
       "      <th>0552</th>\n",
       "      <th>0553</th>\n",
       "      <th>0554</th>\n",
       "      <th>0555</th>\n",
       "      <th>0556</th>\n",
       "      <th>0557</th>\n",
       "      <th>0558</th>\n",
       "      <th>0559</th>\n",
       "      <th>0561</th>\n",
       "      <th>0562</th>\n",
       "      <th>0601</th>\n",
       "      <th>0602</th>\n",
       "      <th>0603</th>\n",
       "      <th>0604</th>\n",
       "      <th>0605</th>\n",
       "      <th>0701</th>\n",
       "      <th>0800</th>\n",
       "      <th>0850</th>\n",
       "      <th>0851</th>\n",
       "      <th>0853</th>\n",
       "      <th>0855</th>\n",
       "      <th>0856</th>\n",
       "      <th>0857</th>\n",
       "      <th>0858</th>\n",
       "      <th>0859</th>\n",
       "      <th>0902</th>\n",
       "      <th>0903</th>\n",
       "      <th>0906</th>\n",
       "      <th>0907</th>\n",
       "      <th>0908</th>\n",
       "      <th>0910</th>\n",
       "      <th>0911</th>\n",
       "      <th>0912</th>\n",
       "      <th>0913</th>\n",
       "      <th>0914</th>\n",
       "      <th>0915</th>\n",
       "      <th>0916</th>\n",
       "      <th>0917</th>\n",
       "      <th>0919</th>\n",
       "      <th>0922</th>\n",
       "      <th>0923</th>\n",
       "      <th>0924</th>\n",
       "      <th>0925</th>\n",
       "      <th>0926</th>\n",
       "      <th>0927</th>\n",
       "      <th>0928</th>\n",
       "      <th>0929</th>\n",
       "      <th>0930</th>\n",
       "      <th>0931</th>\n",
       "      <th>0933</th>\n",
       "      <th>0935</th>\n",
       "      <th>0938</th>\n",
       "      <th>0940</th>\n",
       "      <th>0942</th>\n",
       "      <th>0944</th>\n",
       "      <th>0945</th>\n",
       "      <th>0946</th>\n",
       "      <th>0947</th>\n",
       "      <th>1000</th>\n",
       "      <th>1006</th>\n",
       "      <th>1008</th>\n",
       "      <th>1009</th>\n",
       "      <th>1012</th>\n",
       "      <th>1013</th>\n",
       "      <th>1017</th>\n",
       "      <th>1018</th>\n",
       "      <th>1019</th>\n",
       "      <th>1020</th>\n",
       "      <th>1021</th>\n",
       "      <th>1022</th>\n",
       "      <th>1026</th>\n",
       "      <th>1028</th>\n",
       "      <th>1100</th>\n",
       "      <th>1202</th>\n",
       "      <th>1203</th>\n",
       "      <th>1204</th>\n",
       "      <th>1205</th>\n",
       "      <th>1206</th>\n",
       "      <th>1210</th>\n",
       "      <th>1211</th>\n",
       "      <th>1212</th>\n",
       "      <th>1213</th>\n",
       "      <th>1214</th>\n",
       "      <th>1215</th>\n",
       "      <th>1216</th>\n",
       "      <th>1218</th>\n",
       "      <th>1219</th>\n",
       "      <th>1221</th>\n",
       "      <th>1222</th>\n",
       "      <th>1223</th>\n",
       "      <th>1224</th>\n",
       "      <th>1225</th>\n",
       "      <th>1227</th>\n",
       "      <th>1229</th>\n",
       "      <th>1230</th>\n",
       "      <th>1232</th>\n",
       "      <th>1233</th>\n",
       "      <th>1234</th>\n",
       "      <th>1236</th>\n",
       "      <th>1237</th>\n",
       "      <th>1238</th>\n",
       "      <th>1239</th>\n",
       "      <th>1240</th>\n",
       "      <th>1241</th>\n",
       "      <th>1242</th>\n",
       "      <th>1243</th>\n",
       "      <th>1245</th>\n",
       "      <th>1248</th>\n",
       "      <th>1251</th>\n",
       "      <th>1252</th>\n",
       "      <th>1253</th>\n",
       "      <th>1254</th>\n",
       "      <th>1256</th>\n",
       "      <th>1257</th>\n",
       "      <th>1258</th>\n",
       "      <th>1259</th>\n",
       "      <th>1260</th>\n",
       "      <th>1261</th>\n",
       "      <th>1262</th>\n",
       "      <th>1265</th>\n",
       "      <th>1266</th>\n",
       "      <th>1267</th>\n",
       "      <th>1268</th>\n",
       "      <th>1270</th>\n",
       "      <th>1271</th>\n",
       "      <th>1273</th>\n",
       "      <th>1274</th>\n",
       "      <th>1275</th>\n",
       "      <th>1276</th>\n",
       "      <th>1277</th>\n",
       "      <th>1278</th>\n",
       "      <th>1279</th>\n",
       "      <th>1281</th>\n",
       "      <th>1300</th>\n",
       "      <th>1301</th>\n",
       "      <th>1302</th>\n",
       "      <th>1303</th>\n",
       "      <th>1304</th>\n",
       "      <th>1305</th>\n",
       "      <th>1306</th>\n",
       "      <th>1307</th>\n",
       "      <th>1308</th>\n",
       "      <th>1309</th>\n",
       "      <th>1310</th>\n",
       "      <th>1311</th>\n",
       "      <th>1312</th>\n",
       "      <th>1313</th>\n",
       "      <th>1315</th>\n",
       "      <th>1316</th>\n",
       "      <th>1317</th>\n",
       "      <th>1318</th>\n",
       "      <th>1401</th>\n",
       "      <th>1402</th>\n",
       "      <th>1403</th>\n",
       "      <th>1404</th>\n",
       "      <th>1405</th>\n",
       "      <th>1406</th>\n",
       "      <th>1407</th>\n",
       "      <th>1408</th>\n",
       "      <th>1409</th>\n",
       "      <th>1410</th>\n",
       "      <th>1411</th>\n",
       "      <th>1413</th>\n",
       "      <th>1414</th>\n",
       "      <th>1415</th>\n",
       "      <th>1416</th>\n",
       "      <th>1419</th>\n",
       "      <th>1420</th>\n",
       "      <th>1501</th>\n",
       "      <th>1510</th>\n",
       "      <th>1512</th>\n",
       "      <th>1513</th>\n",
       "      <th>1514</th>\n",
       "      <th>1516</th>\n",
       "      <th>1517</th>\n",
       "      <th>1519</th>\n",
       "      <th>1520</th>\n",
       "      <th>1522</th>\n",
       "      <th>1528</th>\n",
       "      <th>1531</th>\n",
       "      <th>1536</th>\n",
       "      <th>1538</th>\n",
       "      <th>1601</th>\n",
       "      <th>1602</th>\n",
       "      <th>1604</th>\n",
       "      <th>1605</th>\n",
       "      <th>1606</th>\n",
       "      <th>1607</th>\n",
       "      <th>1608</th>\n",
       "      <th>1609</th>\n",
       "      <th>1610</th>\n",
       "      <th>1611</th>\n",
       "      <th>1612</th>\n",
       "      <th>1701</th>\n",
       "      <th>1702</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1807</th>\n",
       "      <th>1809</th>\n",
       "      <th>1810</th>\n",
       "      <th>1811</th>\n",
       "      <th>1812</th>\n",
       "      <th>1813</th>\n",
       "      <th>1814</th>\n",
       "      <th>1815</th>\n",
       "      <th>1816</th>\n",
       "      <th>1817</th>\n",
       "      <th>1818</th>\n",
       "      <th>1819</th>\n",
       "      <th>1820</th>\n",
       "      <th>1821</th>\n",
       "      <th>1822</th>\n",
       "      <th>1823</th>\n",
       "      <th>1824</th>\n",
       "      <th>1900</th>\n",
       "      <th>1901</th>\n",
       "      <th>1902</th>\n",
       "      <th>1903</th>\n",
       "      <th>1906</th>\n",
       "      <th>1908</th>\n",
       "      <th>1910</th>\n",
       "      <th>1912</th>\n",
       "      <th>1913</th>\n",
       "      <th>1914</th>\n",
       "      <th>1915</th>\n",
       "      <th>1916</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2009</th>\n",
       "      <th>2011</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2040</th>\n",
       "      <th>2041</th>\n",
       "      <th>2042</th>\n",
       "      <th>2045</th>\n",
       "      <th>2046</th>\n",
       "      <th>2047</th>\n",
       "      <th>2048</th>\n",
       "      <th>2049</th>\n",
       "      <th>2050</th>\n",
       "      <th>2051</th>\n",
       "      <th>2052</th>\n",
       "      <th>2053</th>\n",
       "      <th>2054</th>\n",
       "      <th>2055</th>\n",
       "      <th>2056</th>\n",
       "      <th>2058</th>\n",
       "      <th>2059</th>\n",
       "      <th>2060</th>\n",
       "      <th>2104</th>\n",
       "      <th>2108</th>\n",
       "      <th>2126</th>\n",
       "      <th>2157</th>\n",
       "      <th>2201</th>\n",
       "      <th>2204</th>\n",
       "      <th>2303</th>\n",
       "      <th>2304</th>\n",
       "      <th>3003</th>\n",
       "      <th>3004</th>\n",
       "      <th>4025</th>\n",
       "      <th>9999</th>\n",
       "      <th>Street_Number_Encoded</th>\n",
       "      <th>Street_Name_Encoded</th>\n",
       "      <th>Street_Type_Encoded</th>\n",
       "      <th>Reporting_District_no_Encoded</th>\n",
       "      <th>Premise_Code_Encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500</td>\n",
       "      <td>None</td>\n",
       "      <td>CARPENTER</td>\n",
       "      <td>AV</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5748.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>45TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>13:45:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>13</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>6581.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>E</td>\n",
       "      <td>MARTIN LUTHER KING JR</td>\n",
       "      <td>BL</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>06:05:00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>55</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900</td>\n",
       "      <td>None</td>\n",
       "      <td>ORO GRANDE</td>\n",
       "      <td>ST</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6581.0</td>\n",
       "      <td>21</td>\n",
       "      <td>5033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100</td>\n",
       "      <td>S</td>\n",
       "      <td>VERMONT</td>\n",
       "      <td>AV</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11:30:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>IC</td>\n",
       "      <td>Property Crimes</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>223</td>\n",
       "      <td>5748.0</td>\n",
       "      <td>47</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Street_Number Direction            Street_Name Street_Type  Latitude  \\\n",
       "0          4500      None              CARPENTER          AV   34.1522   \n",
       "1          None      None                   45TH          ST   34.0028   \n",
       "2           600         E  MARTIN LUTHER KING JR          BL   34.0111   \n",
       "3         14900      None             ORO GRANDE          ST   34.2953   \n",
       "4          7100         S                VERMONT          AV   33.9787   \n",
       "\n",
       "   Longitude Date_Reported Date_Occurred Time_Occurred  Area_ID  \\\n",
       "0  -118.3910    2020-03-09    2020-03-06      18:00:00     15.0   \n",
       "1  -118.2391    2020-02-27    2020-02-27      13:45:00     13.0   \n",
       "2  -118.2653    2020-08-21    2020-08-21      06:05:00     13.0   \n",
       "3  -118.4590    2020-11-08    2020-11-06      18:00:00     19.0   \n",
       "4  -118.2918    2020-02-25    2020-02-25      11:30:00     12.0   \n",
       "\n",
       "   Reporting_District_no  Part 1-2  Victim_Age Victim_Sex Victim_Descent  \\\n",
       "0                 1563.0       1.0        75.0          M              W   \n",
       "1                 1367.0       1.0        41.0          M              H   \n",
       "2                 1343.0       2.0        67.0          M              B   \n",
       "3                 1924.0       1.0        61.0          M              H   \n",
       "4                 1245.0       1.0         0.0          X              X   \n",
       "\n",
       "   Premise_Code  Weapon_Used_Code Status   Crime_Category  \\\n",
       "0         101.0               NaN     IC  Property Crimes   \n",
       "1         216.0             400.0     IC  Property Crimes   \n",
       "2         501.0               NaN     IC  Property Crimes   \n",
       "3         101.0               NaN     IC  Property Crimes   \n",
       "4         401.0             400.0     IC  Property Crimes   \n",
       "\n",
       "   Days_After_Reported  Reported_Day  Reported_Month  Reported_Weekday  \\\n",
       "0                    3             9               3                 0   \n",
       "1                    0            27               2                 3   \n",
       "2                    0            21               8                 4   \n",
       "3                    2             8              11                 6   \n",
       "4                    0            25               2                 1   \n",
       "\n",
       "   Occurred_Day  Occurred_Month  Occurred_Weekday  Occurred_Weekmonth  \\\n",
       "0             6               3                 4                   1   \n",
       "1            27               2                 3                   4   \n",
       "2            21               8                 4                   3   \n",
       "3             6              11                 4                   1   \n",
       "4            25               2                 1                   4   \n",
       "\n",
       "   Occurred_Weekend?  Occurred_Hour  0100  0104  0105  0110  0112  0113  0114  \\\n",
       "0                  0             18   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1                  0             13   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2                  0              6   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3                  0             18   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4                  0             11   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0115  0117  0120  0121  0200  0201  0202  0206  0207  0209  0210  0211  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0212  0213  0216  0217  0220  0301  0302  0303  0304  0305  0307  0308  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0309  0310  0311  0312  0314  0315  0316  0319  0321  0322  0323  0324  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0325  0326  0327  0328      0329  0330  0332  0334  0335  0336  0337  0338  \\\n",
       "0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0  0.590094   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0  0.664081   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0339  0340  0341  0342  0343      0344  0345  0346  0347  0349  0350  0351  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.180678   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.227903   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "       0352  0353  0354  0355  0356  0357  0358  0359  0360  0361  0362  0363  \\\n",
       "0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  0.398643   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0364  0365  0366  0368  0369  0370      0371  0372  0373  0374  0375  0377  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0  0.509267   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0378  0379  0380  0381  0382  0383  0384  0385  0386  0387  0388  0389  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0390  0391  0392  0394  0395  0396  0397  0398  0399      0400  0401  0402  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.369837   0.0   0.0   \n",
       "\n",
       "   0404  0405  0406  0407  0408  0409  0410  0411  0413  0414      0416  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.240850   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.303802   \n",
       "\n",
       "       0417  0418  0419  0420  0421  0422  0424  0425  0426  0429  0430  0431  \\\n",
       "0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  0.412012   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4  0.519701   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0432  0433  0434  0437  0440  0442  0443  0444  0445      0446  0447  0448  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.370276   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   \n",
       "\n",
       "   0449  0450  0451  0500  0501  0503  0505  0507  0508  0509  0510  0512  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0513  0514  0515  0516  0519  0520  0522  0525  0526  0527  0528  0529  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0531  0532  0533  0534  0536  0537  0538  0541  0542  0543  0544  0545  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0547  0549  0550  0551  0552  0553  0554  0555  0556  0557  0558  0559  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0561  0562  0601  0602  0603  0604  0605  0701  0800  0850  0851  0853  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0855  0856  0857  0858  0859  0902  0903     0906  0907  0908  0910  0911  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.38948   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.00000   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0912  0913  0914  0915  0916  0917  0919  0922  0923  0924  0925  0926  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   0927  0928  0929  0930  0931  0933  0935  0938  0940  0942  0944      0945  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.000000   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.630196   \n",
       "\n",
       "   0946  0947  1000  1006  1008  1009  1012  1013  1017  1018  1019  1020  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1021  1022  1026  1028  1100      1202  1203  1204  1205  1206  1210  1211  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.807334   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1212  1213  1214  1215  1216  1218  1219  1221  1222  1223  1224  1225  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1227  1229  1230  1232  1233  1234  1236  1237  1238  1239  1240  1241  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1242  1243  1245  1248  1251  1252  1253  1254  1256  1257  1258  1259  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1260  1261  1262  1265  1266  1267  1268  1270  1271  1273  1274  1275  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1276  1277  1278  1279  1281      1300  1301  1302  1303  1304  1305  1306  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0  0.747661   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1307  1308  1309  1310  1311  1312  1313  1315  1316  1317  1318  1401  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1402  1403  1404  1405  1406  1407  1408  1409  1410  1411  1413  1414  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1415  1416  1419  1420  1501  1510  1512  1513  1514  1516  1517  1519  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1520  1522  1528  1531  1536  1538  1601  1602  1604  1605  1606  1607  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1608  1609  1610  1611  1612  1701  1702  1801  1802  1803  1804  1805  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1807  1809  1810  1811  1812  1813  1814  1815  1816  1817  1818  1819  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1820  1821      1822  1823  1824  1900  1901  1902  1903  1906  1908  1910  \\\n",
       "0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0  0.180347   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0  0.000000   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0  0.227485   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1912  1913  1914  1915  1916  2000  2001  2002  2003  2004  2005  2006  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2007  2009  2011  2013  2014  2018  2019  2020  2021  2022  2023  2024  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2025  2027  2028  2029  2030  2031  2032  2033  2034  2035  2036  2037  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2038  2040  2041  2042  2045  2046  2047  2048  2049  2050  2051  2052  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2053  2054  2055  2056  2058  2059  2060  2104  2108  2126  2157  2201  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   2204  2303  2304  3003  3004  4025  9999  Street_Number_Encoded  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0                   97.0   \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0                    NaN   \n",
       "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0                  477.0   \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0                   34.0   \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0                   75.0   \n",
       "\n",
       "   Street_Name_Encoded  Street_Type_Encoded  Reporting_District_no_Encoded  \\\n",
       "0                    3               5748.0                             12   \n",
       "1                   12               6581.0                             29   \n",
       "2                   55               3724.0                             29   \n",
       "3                    3               6581.0                             21   \n",
       "4                  223               5748.0                             47   \n",
       "\n",
       "   Premise_Code_Encoded  \n",
       "0                  5033  \n",
       "1                     1  \n",
       "2                  3379  \n",
       "3                  5033  \n",
       "4                   111  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LocationTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, location_col='Location'):\n",
    "        self.location_col = location_col\n",
    "\n",
    "    def extract_location_data(self, s):\n",
    "        street_number = direction = street_name = street_type = None\n",
    "        try:  # street number\n",
    "            nt = s.split(' ')[0]\n",
    "            street_number = nt if int(nt.isdigit()) else None\n",
    "        except: pass\n",
    "        try:  # direction\n",
    "            nt = [x.strip() for x in s.split(' ') if x.strip()]\n",
    "            for x in nt:\n",
    "                if x.isdigit(): continue\n",
    "                elif len(x) > 1: break\n",
    "                if x in ['N', 'S', 'E', 'W']:\n",
    "                    direction = x\n",
    "                    break\n",
    "        except: pass\n",
    "        try:  # street type\n",
    "            nt = s.split('   ')[-1].strip()\n",
    "            street_type = nt if len(nt) == 2 and not(nt.isdigit()) else None\n",
    "        except: pass\n",
    "        street_name = None  # street name\n",
    "        nt = [x.strip() for x in s.split('  ') if x.strip()]\n",
    "        for x in nt:\n",
    "            test_x = [x.strip() for x in x.split(' ') if x.strip()]\n",
    "            if len(test_x) == 1 and not(street_number and street_number == test_x[0]) and not(street_type and street_type == test_x[0]):\n",
    "                street_name = x\n",
    "                break\n",
    "            elif not(street_number and street_number in test_x):\n",
    "                street_name = x\n",
    "                break\n",
    "        \n",
    "        return street_number, direction, street_name, street_type\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        parsed_data = []\n",
    "        for idx, row in X.iterrows():\n",
    "            string = row[self.location_col]\n",
    "            street_number, direction, street_name, street_type = self.extract_location_data(string)\n",
    "            parsed_data.append({\n",
    "                'Street_Number': street_number,\n",
    "                'Direction': direction,\n",
    "                'Street_Name': street_name,\n",
    "                'Street_Type': street_type\n",
    "            })\n",
    "        location_df = pd.DataFrame(parsed_data, index=X.index)\n",
    "        return pd.concat([location_df, X.drop('Location',axis=1)], axis=1)\n",
    "    \n",
    "class ExtractDateInfo(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, date_reported_col='Date_Reported', date_occurred_col='Date_Occurred', time_occurred_col='Time_Occurred'):\n",
    "        self.date_reported_col = date_reported_col\n",
    "        self.date_occurred_col = date_occurred_col\n",
    "        self.time_occurred_col = time_occurred_col\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = X.copy()\n",
    "        X[self.date_reported_col] = pd.to_datetime(X[self.date_reported_col], format='%m/%d/%Y %I:%M:%S %p')\n",
    "        X[self.date_occurred_col] = pd.to_datetime(X[self.date_occurred_col], format='%m/%d/%Y %I:%M:%S %p')\n",
    "\n",
    "        def convert_time_occurred(time):\n",
    "            time_str = f\"{int(time):04d}\"\n",
    "            return f\"{time_str[:2]}:{time_str[2:]}:00\"\n",
    "\n",
    "        X[self.time_occurred_col] = X[self.time_occurred_col].apply(convert_time_occurred)\n",
    "        X['Days_After_Reported'] = (X[self.date_reported_col] - X[self.date_occurred_col]).dt.days\n",
    "        X['Reported_Day'] = X[self.date_reported_col].dt.day\n",
    "        X['Reported_Month'] = X[self.date_reported_col].dt.month\n",
    "        X['Reported_Weekday'] = X[self.date_reported_col].dt.weekday\n",
    "        X['Occurred_Day'] = X[self.date_occurred_col].dt.day\n",
    "        X['Occurred_Month'] = X[self.date_occurred_col].dt.month\n",
    "        X['Occurred_Weekday'] = X[self.date_occurred_col].dt.weekday\n",
    "        X['Occurred_Weekmonth'] = X[self.date_occurred_col].apply(lambda d: (d.day - 1) // 7 + 1)\n",
    "        X['Occurred_Weekend?'] = np.where(X['Occurred_Weekday'].isin([5, 6]), 1, 0)\n",
    "        X['Occurred_Hour'] = pd.to_datetime(X[self.time_occurred_col], format='%H:%M:%S').dt.hour\n",
    "\n",
    "        return X\n",
    "    \n",
    "class TfidfVectorizerTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, column):\n",
    "        self.column = column\n",
    "        self.vectorizer = TfidfVectorizer()\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        X = X.copy()\n",
    "        X[self.column] = X[self.column].fillna('')\n",
    "        self.vectorizer.fit(X[self.column])\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = X.copy()\n",
    "        X[self.column] = X[self.column].fillna('')\n",
    "        modus = self.vectorizer.transform(X[self.column])\n",
    "        modus_data = pd.DataFrame(data=modus.toarray(), columns=self.vectorizer.get_feature_names_out(), index=X.index)\n",
    "        X = X.drop(self.column, axis=1)\n",
    "        X = pd.concat([X, modus_data], axis=1)\n",
    "        return X\n",
    "    \n",
    "class FlattenAndCountVectorizer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self.vectorizer = CountVectorizer()\n",
    "    def fit(self, X, y=None):\n",
    "        # Ensure X is a 1D array of strings\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            X = X.iloc[:, 0].values.flatten()\n",
    "        else:\n",
    "            X = X.flatten()\n",
    "        self.vectorizer.fit(X)\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        # Ensure X is a 1D array of strings\n",
    "        if isinstance(X, pd.DataFrame):\n",
    "            X = X.iloc[:, 0].values.flatten()\n",
    "        else:\n",
    "            X = X.flatten()\n",
    "        X_vec = self.vectorizer.transform(X)\n",
    "        return pd.DataFrame(X_vec.toarray(), columns=self.vectorizer.get_feature_names_out())\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['Victim_Age','Occurred_Hour']\n",
    "nom_cols = ['Reporting_District_no','Victim_Sex','Victim_Descent','Premise_Code','Weapon_Used_Code','Status']\n",
    "ord_cols = ['Part 1-2']\n",
    "mullab_cols = ['Modus_Operandi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;extract_data&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;extract_location&#x27;, LocationTransformer()),\n",
       "                                 (&#x27;extract_date&#x27;, ExtractDateInfo())])),\n",
       "                (&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Victim_Age&#x27;,\n",
       "                                                   &#x27;Occurred_Hour&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;mo...\n",
       "                                                  [&#x27;Reporting_District_no&#x27;,\n",
       "                                                   &#x27;Victim_Sex&#x27;,\n",
       "                                                   &#x27;Victim_Descent&#x27;,\n",
       "                                                   &#x27;Premise_Code&#x27;,\n",
       "                                                   &#x27;Weapon_Used_Code&#x27;,\n",
       "                                                   &#x27;Status&#x27;]),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OrdinalEncoder())]),\n",
       "                                                  [&#x27;Part 1-2&#x27;]),\n",
       "                                                 (&#x27;mlb&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;vectorizer&#x27;,\n",
       "                                                                   FlattenAndCountVectorizer())]),\n",
       "                                                  [&#x27;Modus_Operandi&#x27;])]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;extract_data&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;extract_location&#x27;, LocationTransformer()),\n",
       "                                 (&#x27;extract_date&#x27;, ExtractDateInfo())])),\n",
       "                (&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Victim_Age&#x27;,\n",
       "                                                   &#x27;Occurred_Hour&#x27;]),\n",
       "                                                 (&#x27;nom&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;mo...\n",
       "                                                  [&#x27;Reporting_District_no&#x27;,\n",
       "                                                   &#x27;Victim_Sex&#x27;,\n",
       "                                                   &#x27;Victim_Descent&#x27;,\n",
       "                                                   &#x27;Premise_Code&#x27;,\n",
       "                                                   &#x27;Weapon_Used_Code&#x27;,\n",
       "                                                   &#x27;Status&#x27;]),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OrdinalEncoder())]),\n",
       "                                                  [&#x27;Part 1-2&#x27;]),\n",
       "                                                 (&#x27;mlb&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;vectorizer&#x27;,\n",
       "                                                                   FlattenAndCountVectorizer())]),\n",
       "                                                  [&#x27;Modus_Operandi&#x27;])]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;extract_data: Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for extract_data: Pipeline</span></a></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;extract_location&#x27;, LocationTransformer()),\n",
       "                (&#x27;extract_date&#x27;, ExtractDateInfo())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">LocationTransformer</label><div class=\"sk-toggleable__content \"><pre>LocationTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ExtractDateInfo</label><div class=\"sk-toggleable__content \"><pre>ExtractDateInfo()</pre></div> </div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Victim_Age&#x27;, &#x27;Occurred_Hour&#x27;]),\n",
       "                                (&#x27;nom&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Reporting_District_no&#x27;, &#x27;Victim_Sex&#x27;,\n",
       "                                  &#x27;Victim_Descent&#x27;, &#x27;Premise_Code&#x27;,\n",
       "                                  &#x27;Weapon_Used_Code&#x27;, &#x27;Status&#x27;]),\n",
       "                                (&#x27;ord&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;, OrdinalEncoder())]),\n",
       "                                 [&#x27;Part 1-2&#x27;]),\n",
       "                                (&#x27;mlb&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;vectorizer&#x27;,\n",
       "                                                  FlattenAndCountVectorizer())]),\n",
       "                                 [&#x27;Modus_Operandi&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;Victim_Age&#x27;, &#x27;Occurred_Hour&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">nom</label><div class=\"sk-toggleable__content \"><pre>[&#x27;Reporting_District_no&#x27;, &#x27;Victim_Sex&#x27;, &#x27;Victim_Descent&#x27;, &#x27;Premise_Code&#x27;, &#x27;Weapon_Used_Code&#x27;, &#x27;Status&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ord</label><div class=\"sk-toggleable__content \"><pre>[&#x27;Part 1-2&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">mlb</label><div class=\"sk-toggleable__content \"><pre>[&#x27;Modus_Operandi&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(fill_value=&#x27;&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">FlattenAndCountVectorizer</label><div class=\"sk-toggleable__content \"><pre>FlattenAndCountVectorizer()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('extract_data',\n",
       "                 Pipeline(steps=[('extract_location', LocationTransformer()),\n",
       "                                 ('extract_date', ExtractDateInfo())])),\n",
       "                ('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Victim_Age',\n",
       "                                                   'Occurred_Hour']),\n",
       "                                                 ('nom',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='mo...\n",
       "                                                  ['Reporting_District_no',\n",
       "                                                   'Victim_Sex',\n",
       "                                                   'Victim_Descent',\n",
       "                                                   'Premise_Code',\n",
       "                                                   'Weapon_Used_Code',\n",
       "                                                   'Status']),\n",
       "                                                 ('ord',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OrdinalEncoder())]),\n",
       "                                                  ['Part 1-2']),\n",
       "                                                 ('mlb',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(fill_value='',\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('vectorizer',\n",
       "                                                                   FlattenAndCountVectorizer())]),\n",
       "                                                  ['Modus_Operandi'])]))])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_data = Pipeline(steps=[\n",
    "        ('extract_location', LocationTransformer()),\n",
    "        ('extract_date', ExtractDateInfo()),\n",
    "])\n",
    "num_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler()),\n",
    "])\n",
    "nom_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore')),\n",
    "])\n",
    "ord_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OrdinalEncoder()),\n",
    "])\n",
    "mulLable_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='')),\n",
    "    ('vectorizer', FlattenAndCountVectorizer()),\n",
    "])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', num_transformer, num_cols),\n",
    "        ('nom', nom_transformer, nom_cols),\n",
    "        ('ord', ord_transformer, ord_cols),\n",
    "        ('mlb', mulLable_transformer, mullab_cols)\n",
    "    ]\n",
    ")\n",
    "model_pipeline = Pipeline(steps=[\n",
    "    ('extract_data', extract_data),\n",
    "    ('preprocessor', preprocessor),\n",
    "])\n",
    "model_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_pipeline.fit(test_df)\n",
    "# X = model_pipeline.transform(test_df)\n",
    "# X = pd.DataFrame(X.toarray(), columns=model_pipeline.named_steps['preprocessor'].get_feature_names_out())\n",
    "# X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df.drop('Crime_Category',axis=1)\n",
    "y = train_df['Crime_Category']\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model_pipeline.fit(X_train)\n",
    "X_train_trans = model_pipeline.transform(X_train)\n",
    "X_val_trans = model_pipeline.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# models = {\n",
    "#     'RandomForest': RandomForestClassifier(),\n",
    "#     'LogisticRegression': LogisticRegression(max_iter=1000),\n",
    "#     'SVC': SVC(),\n",
    "#     'KNN': KNeighborsClassifier(),\n",
    "#     # 'NaiveBayes': GaussianNB(), #\n",
    "#     'DecisionTreeClassifier': DecisionTreeClassifier(),\n",
    "#     'GradientBoostingClassifier': GradientBoostingClassifier(),\n",
    "# }\n",
    "# best_model = None; best_accuracy = 0; best_model_name = \"\"\n",
    "# for model_name, model in models.items():\n",
    "#     model.fit(X_train_trans, y_train)\n",
    "#     val_predictions = model.predict(X_val_trans)\n",
    "#     accuracy = accuracy_score(y_val, val_predictions)\n",
    "#     print(f\"Validation Accuracy for {model_name}: {accuracy}\\n\")\n",
    "#     if accuracy > best_accuracy:\n",
    "#         best_accuracy = accuracy\n",
    "#         best_model = model\n",
    "#         best_model_name = model_name\n",
    "# print(f\"Best Model: {best_model_name}\")\n",
    "# print(f\"Best Model Accuracy: {best_accuracy}\") # 0.94775"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold accuracy: 0.948\n",
      "Fold accuracy: 0.935\n",
      "Fold accuracy: 0.954\n",
      "Fold accuracy: 0.957\n",
      "Fold accuracy: 0.95\n",
      "Fold accuracy: 0.955\n",
      "Fold accuracy: 0.933\n",
      "Fold accuracy: 0.944\n",
      "Fold accuracy: 0.95\n",
      "Fold accuracy: 0.962\n",
      "Fold accuracy: 0.95\n",
      "Fold accuracy: 0.963\n",
      "Fold accuracy: 0.944\n",
      "Fold accuracy: 0.949\n",
      "Fold accuracy: 0.938\n",
      "Fold accuracy: 0.953\n",
      "Fold accuracy: 0.956\n",
      "Fold accuracy: 0.955\n",
      "Fold accuracy: 0.948\n",
      "Fold accuracy: 0.962\n",
      "Average accuracy: 0.9502999999999998\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "# Define the k-fold cross-validator\n",
    "kf = KFold(n_splits=20, shuffle=True, random_state=42)\n",
    "\n",
    "# Initialize the model\n",
    "model = SVC()\n",
    "\n",
    "# List to store the accuracy for each fold\n",
    "accuracies = []\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "for train_index, test_index in kf.split(X):\n",
    "    # Split the data into training and testing sets\n",
    "    X_train, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
    "    model_pipeline.fit(X_train)\n",
    "    X_train_trans = model_pipeline.transform(X_train)\n",
    "    X_val_trans = model_pipeline.transform(X_val)\n",
    "\n",
    "    # Train the model\n",
    "    model.fit(X_train_trans, y_train)\n",
    "\n",
    "    # Predict on the test set\n",
    "    y_pred = model.predict(X_val_trans)\n",
    "\n",
    "    # Calculate the accuracy\n",
    "    accuracy = accuracy_score(y_val, y_pred)\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "    print(f'Fold accuracy: {accuracy}')\n",
    "\n",
    "# Calculate and print the average accuracy\n",
    "average_accuracy = np.mean(accuracies)\n",
    "print(f'Average accuracy: {average_accuracy}') # 0.953"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X has 1865 features, but SVC is expecting 1890 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[176], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m X_train_trans \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mtransform(X_train)\n\u001b[0;32m      6\u001b[0m X_val_trans \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mtransform(X_val)\n\u001b[1;32m----> 8\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_val_trans\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m accuracy \u001b[38;5;241m=\u001b[39m accuracy_score(y_val, y_pred)\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(accuracy)\n",
      "File \u001b[1;32mc:\\Users\\parna\\Documents\\GitClones\\MLPproject\\venv\\Lib\\site-packages\\sklearn\\svm\\_base.py:813\u001b[0m, in \u001b[0;36mBaseSVC.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    811\u001b[0m     y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecision_function(X), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    812\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 813\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_\u001b[38;5;241m.\u001b[39mtake(np\u001b[38;5;241m.\u001b[39masarray(y, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp))\n",
      "File \u001b[1;32mc:\\Users\\parna\\Documents\\GitClones\\MLPproject\\venv\\Lib\\site-packages\\sklearn\\svm\\_base.py:428\u001b[0m, in \u001b[0;36mBaseLibSVM.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    412\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    413\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Perform regression on samples in X.\u001b[39;00m\n\u001b[0;32m    414\u001b[0m \n\u001b[0;32m    415\u001b[0m \u001b[38;5;124;03m    For an one-class model, +1 (inlier) or -1 (outlier) is returned.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[38;5;124;03m        The predicted values.\u001b[39;00m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 428\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_for_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    429\u001b[0m     predict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sparse_predict \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sparse \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dense_predict\n\u001b[0;32m    430\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m predict(X)\n",
      "File \u001b[1;32mc:\\Users\\parna\\Documents\\GitClones\\MLPproject\\venv\\Lib\\site-packages\\sklearn\\svm\\_base.py:606\u001b[0m, in \u001b[0;36mBaseLibSVM._validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    603\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel):\n\u001b[1;32m--> 606\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    608\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    609\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    610\u001b[0m \u001b[43m        \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    611\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    612\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    613\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sparse \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sp\u001b[38;5;241m.\u001b[39missparse(X):\n\u001b[0;32m    616\u001b[0m     X \u001b[38;5;241m=\u001b[39m sp\u001b[38;5;241m.\u001b[39mcsr_matrix(X)\n",
      "File \u001b[1;32mc:\\Users\\parna\\Documents\\GitClones\\MLPproject\\venv\\Lib\\site-packages\\sklearn\\base.py:654\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    651\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    653\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 654\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_n_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "File \u001b[1;32mc:\\Users\\parna\\Documents\\GitClones\\MLPproject\\venv\\Lib\\site-packages\\sklearn\\base.py:443\u001b[0m, in \u001b[0;36mBaseEstimator._check_n_features\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_features \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_:\n\u001b[1;32m--> 443\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    444\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_features\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features, but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    445\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis expecting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features as input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    446\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: X has 1865 features, but SVC is expecting 1890 features as input."
     ]
    }
   ],
   "source": [
    "X = train_df.drop('Crime_Category',axis=1)\n",
    "y = train_df['Crime_Category']\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model_pipeline.fit(X_train)\n",
    "X_train_trans = model_pipeline.transform(X_train)\n",
    "X_val_trans = model_pipeline.transform(X_val)\n",
    "\n",
    "model.fit(X_train_trans, y_train)\n",
    "y_pred = model.predict(X_val_trans)\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper Parameters Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  44.0s\n",
      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  50.7s\n",
      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  42.9s\n",
      "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  43.1s\n",
      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  51.2s\n",
      "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.919 total time=  10.7s\n",
      "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.917 total time=  10.7s\n",
      "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.915 total time=  11.0s\n",
      "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.917 total time=  11.2s\n",
      "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.922 total time=  10.8s\n",
      "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.857 total time=  12.3s\n",
      "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.863 total time=  11.6s\n",
      "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.858 total time=  12.6s\n",
      "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.858 total time=  12.7s\n",
      "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.864 total time=  10.8s\n",
      "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.642 total time=  13.8s\n",
      "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.643 total time=  14.0s\n",
      "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.643 total time=  14.0s\n",
      "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.652 total time=  13.9s\n",
      "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.642 total time=  13.9s\n",
      "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.7s\n",
      "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.6s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.7s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.6s\n",
      "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.5s\n",
      "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  49.6s\n",
      "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  50.1s\n",
      "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.593 total time=  48.8s\n",
      "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  48.6s\n",
      "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.592 total time=  50.4s\n",
      "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.2s\n",
      "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.3s\n",
      "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
      "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.948 total time=   6.3s\n",
      "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.3s\n",
      "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.935 total time=   5.5s\n",
      "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.930 total time=   5.6s\n",
      "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.932 total time=   5.5s\n",
      "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.927 total time=   5.6s\n",
      "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.937 total time=   5.6s\n",
      "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.869 total time=  10.2s\n",
      "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.874 total time=  11.4s\n",
      "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.871 total time=  10.3s\n",
      "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.871 total time=  10.2s\n",
      "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.876 total time=  10.2s\n",
      "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.645 total time=  13.7s\n",
      "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.647 total time=  13.7s\n",
      "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.647 total time=  13.8s\n",
      "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.655 total time=  13.8s\n",
      "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.648 total time=  13.7s\n",
      "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
      "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.602 total time= 1.1min\n",
      "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
      "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
      "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.4s\n",
      "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
      "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.952 total time=   6.3s\n",
      "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.951 total time=   3.8s\n",
      "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.7s\n",
      "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.950 total time=   3.7s\n",
      "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.950 total time=   3.7s\n",
      "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.952 total time=   3.7s\n",
      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.936 total time=   5.4s\n",
      "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.930 total time=   5.4s\n",
      "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.933 total time=   5.3s\n",
      "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.930 total time=   5.4s\n",
      "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.936 total time=   5.3s\n",
      "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.872 total time=  10.1s\n",
      "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.874 total time=  10.2s\n",
      "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.872 total time=  10.1s\n",
      "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.873 total time=  10.1s\n",
      "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.877 total time=  10.1s\n",
      "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
      "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.1min\n",
      "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.602 total time= 1.1min\n",
      "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.1min\n",
      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.5s\n",
      "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.951 total time=   3.6s\n",
      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.941 total time=   3.8s\n",
      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.948 total time=   3.7s\n",
      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.7s\n",
      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.8s\n",
      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.949 total time=   3.5s\n",
      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.944 total time=   3.5s\n",
      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.948 total time=   3.4s\n",
      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.947 total time=   3.6s\n",
      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.950 total time=   3.5s\n",
      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.936 total time=   5.4s\n",
      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.930 total time=   5.5s\n",
      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.933 total time=   5.3s\n",
      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.930 total time=   5.4s\n",
      "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.935 total time=   5.3s\n",
      "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
      "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.602 total time= 1.0min\n",
      "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
      "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
      "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.5s\n",
      "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.4s\n",
      "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.5s\n",
      "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   8.8s\n",
      "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.949 total time=   7.7s\n",
      "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.939 total time=   5.5s\n",
      "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.944 total time=   4.7s\n",
      "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.942 total time=   4.1s\n",
      "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.944 total time=   5.3s\n",
      "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.944 total time=   4.2s\n",
      "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.940 total time=   3.6s\n",
      "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.943 total time=   3.9s\n",
      "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.938 total time=   3.6s\n",
      "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.938 total time=   3.6s\n",
      "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.948 total time=   3.6s\n",
      "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.944 total time=   4.3s\n",
      "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.948 total time=   4.6s\n",
      "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.946 total time=   5.2s\n",
      "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.949 total time=   4.9s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
       "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
       "                         &#x27;gamma&#x27;: [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
       "             verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10, gamma=0.1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10, gamma=0.1)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV \n",
    "  \n",
    "# defining parameter range \n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10, 100, 1000],  \n",
    "    'gamma': [1, 0.1, 0.01, 0.001, 0.0001], \n",
    "    'kernel': ['rbf']\n",
    "    }  \n",
    "  \n",
    "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3) \n",
    "  \n",
    "X = train_df.drop('Crime_Category',axis=1)\n",
    "y = train_df['Crime_Category']\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model_pipeline.fit(X_train)\n",
    "X_train_trans = model_pipeline.transform(X_train)\n",
    "X_val_trans = model_pipeline.transform(X_val)\n",
    "\n",
    "grid.fit(X_train_trans, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
    "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  44.0s\n",
    "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  50.7s\n",
    "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  42.9s\n",
    "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  43.1s\n",
    "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.585 total time=  51.2s\n",
    "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.919 total time=  10.7s\n",
    "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.917 total time=  10.7s\n",
    "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.915 total time=  11.0s\n",
    "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.917 total time=  11.2s\n",
    "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.922 total time=  10.8s\n",
    "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.857 total time=  12.3s\n",
    "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.863 total time=  11.6s\n",
    "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.858 total time=  12.6s\n",
    "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.858 total time=  12.7s\n",
    "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.864 total time=  10.8s\n",
    "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.642 total time=  13.8s\n",
    "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.643 total time=  14.0s\n",
    "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.643 total time=  14.0s\n",
    "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.652 total time=  13.9s\n",
    "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.642 total time=  13.9s\n",
    "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.7s\n",
    "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.6s\n",
    "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.7s\n",
    "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.6s\n",
    "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.585 total time=  13.5s\n",
    "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  49.6s\n",
    "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  50.1s\n",
    "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.593 total time=  48.8s\n",
    "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.591 total time=  48.6s\n",
    "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.592 total time=  50.4s\n",
    "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.2s\n",
    "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.3s\n",
    "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
    "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.948 total time=   6.3s\n",
    "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.949 total time=   6.3s\n",
    "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.935 total time=   5.5s\n",
    "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.930 total time=   5.6s\n",
    "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.932 total time=   5.5s\n",
    "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.927 total time=   5.6s\n",
    "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.937 total time=   5.6s\n",
    "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.869 total time=  10.2s\n",
    "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.874 total time=  11.4s\n",
    "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.871 total time=  10.3s\n",
    "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.871 total time=  10.2s\n",
    "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.876 total time=  10.2s\n",
    "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.645 total time=  13.7s\n",
    "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.647 total time=  13.7s\n",
    "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.647 total time=  13.8s\n",
    "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.655 total time=  13.8s\n",
    "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.648 total time=  13.7s\n",
    "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
    "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.602 total time= 1.1min\n",
    "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
    "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
    "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.4s\n",
    "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
    "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.952 total time=   6.3s\n",
    "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.951 total time=   3.8s\n",
    "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.7s\n",
    "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.950 total time=   3.7s\n",
    "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.950 total time=   3.7s\n",
    "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.952 total time=   3.7s\n",
    "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.936 total time=   5.4s\n",
    "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.930 total time=   5.4s\n",
    "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.933 total time=   5.3s\n",
    "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.930 total time=   5.4s\n",
    "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.936 total time=   5.3s\n",
    "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.872 total time=  10.1s\n",
    "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.874 total time=  10.2s\n",
    "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.872 total time=  10.1s\n",
    "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.873 total time=  10.1s\n",
    "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.877 total time=  10.1s\n",
    "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
    "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.1min\n",
    "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.602 total time= 1.1min\n",
    "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.598 total time= 1.1min\n",
    "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
    "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.4s\n",
    "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.5s\n",
    "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
    "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.3s\n",
    "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.951 total time=   3.6s\n",
    "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.941 total time=   3.8s\n",
    "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.948 total time=   3.7s\n",
    "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.7s\n",
    "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.946 total time=   3.8s\n",
    "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.949 total time=   3.5s\n",
    "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.944 total time=   3.5s\n",
    "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.948 total time=   3.4s\n",
    "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.947 total time=   3.6s\n",
    "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.950 total time=   3.5s\n",
    "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.936 total time=   5.4s\n",
    "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.930 total time=   5.5s\n",
    "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.933 total time=   5.3s\n",
    "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.930 total time=   5.4s\n",
    "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.935 total time=   5.3s\n",
    "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.596 total time= 1.1min\n",
    "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.602 total time= 1.0min\n",
    "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.598 total time= 1.0min\n",
    "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.4s\n",
    "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.947 total time=   6.5s\n",
    "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.950 total time=   6.4s\n",
    "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   6.5s\n",
    "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.953 total time=   8.8s\n",
    "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.949 total time=   7.7s\n",
    "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.939 total time=   5.5s\n",
    "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.944 total time=   4.7s\n",
    "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.942 total time=   4.1s\n",
    "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.944 total time=   5.3s\n",
    "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.944 total time=   4.2s\n",
    "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.940 total time=   3.6s\n",
    "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.943 total time=   3.9s\n",
    "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.938 total time=   3.6s\n",
    "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.938 total time=   3.6s\n",
    "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.948 total time=   3.6s\n",
    "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.944 total time=   4.3s\n",
    "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.948 total time=   4.6s\n",
    "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.946 total time=   5.2s\n",
    "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.949 total time=   4.9s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "SVC(C=10, gamma=0.1)\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)\n",
    "\n",
    "# {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
    "# SVC(C=10, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               precision    recall  f1-score   support\n",
      "\n",
      "       Crimes against Persons       0.57      0.53      0.55        32\n",
      "  Crimes against Public Order       0.83      0.84      0.84       374\n",
      "Fraud and White-Collar Crimes       0.93      0.91      0.92       267\n",
      "                 Other Crimes       0.64      0.26      0.37        35\n",
      "              Property Crimes       0.99      0.99      0.99      2303\n",
      "               Violent Crimes       0.92      0.95      0.94       989\n",
      "\n",
      "                     accuracy                           0.95      4000\n",
      "                    macro avg       0.81      0.75      0.77      4000\n",
      "                 weighted avg       0.95      0.95      0.95      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "grid_predictions = grid.predict(X_val_trans) \n",
    "  \n",
    "print(classification_report(y_val, grid_predictions))\n",
    "\n",
    "#                                precision    recall  f1-score   support\n",
    "\n",
    "#        Crimes against Persons       0.57      0.53      0.55        32\n",
    "#   Crimes against Public Order       0.83      0.84      0.84       374\n",
    "# Fraud and White-Collar Crimes       0.93      0.91      0.92       267\n",
    "#                  Other Crimes       0.64      0.26      0.37        35\n",
    "#               Property Crimes       0.99      0.99      0.99      2303\n",
    "#                Violent Crimes       0.92      0.95      0.94       989\n",
    "\n",
    "#                      accuracy                           0.95      4000\n",
    "#                     macro avg       0.81      0.75      0.77      4000\n",
    "#                  weighted avg       0.95      0.95      0.95      4000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94925\n"
     ]
    }
   ],
   "source": [
    "best_svc_params = {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
    "best_model = SVC(**best_svc_params)\n",
    "\n",
    "X = train_df.drop('Crime_Category',axis=1)\n",
    "y = train_df['Crime_Category']\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model_pipeline.fit(X_train)\n",
    "X_train_trans = model_pipeline.transform(X_train)\n",
    "X_val_trans = model_pipeline.transform(X_val)\n",
    "\n",
    "best_model.fit(X_train_trans, y_train)\n",
    "val_predictions = best_model.predict(X_val_trans)\n",
    "accuracy = accuracy_score(y_val, val_predictions)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 2)\n"
     ]
    }
   ],
   "source": [
    "test_data_trans = model_pipeline.transform(test_df)\n",
    "test_predictions = best_model.predict(test_data_trans)\n",
    "\n",
    "submission = pd.DataFrame(\n",
    "    {\n",
    "        \"ID\": test_df.index+1,\n",
    "        \"Crime_Category\": test_predictions,\n",
    "    }\n",
    ")\n",
    "submission.to_csv(\"submission.csv\", index=False)\n",
    "print(submission.shape)"
   ]
  }
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