Final_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (33) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv(\"Data-Collisions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Rows: 194673\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    136485\n",
       "2     58188\n",
       "Name: SEVERITYCODE, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total Rows:\", len(data))\n",
    "data[\"SEVERITYCODE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Data Types</th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PEDROWNOTGRNT</th>\n",
       "      <td>object</td>\n",
       "      <td>190006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXCEPTRSNDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>189035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPEEDING</th>\n",
       "      <td>object</td>\n",
       "      <td>185340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INATTENTIONIND</th>\n",
       "      <td>object</td>\n",
       "      <td>164868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTKEY</th>\n",
       "      <td>float64</td>\n",
       "      <td>129603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXCEPTRSNCODE</th>\n",
       "      <td>object</td>\n",
       "      <td>109862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOTCOLNUM</th>\n",
       "      <td>float64</td>\n",
       "      <td>79737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>6329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>float64</td>\n",
       "      <td>5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>float64</td>\n",
       "      <td>5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER</th>\n",
       "      <td>object</td>\n",
       "      <td>5081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROADCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNDERINFL</th>\n",
       "      <td>object</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCATION</th>\n",
       "      <td>object</td>\n",
       "      <td>2677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADDRTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <td>object</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCDTTM</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCDATE</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE.1</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUS</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPORTNO</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLDETKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Data Types  Null Values\n",
       "PEDROWNOTGRNT      object       190006\n",
       "EXCEPTRSNDESC      object       189035\n",
       "SPEEDING           object       185340\n",
       "INATTENTIONIND     object       164868\n",
       "INTKEY            float64       129603\n",
       "EXCEPTRSNCODE      object       109862\n",
       "SDOTCOLNUM        float64        79737\n",
       "JUNCTIONTYPE       object         6329\n",
       "X                 float64         5334\n",
       "Y                 float64         5334\n",
       "LIGHTCOND          object         5170\n",
       "WEATHER            object         5081\n",
       "ROADCOND           object         5012\n",
       "ST_COLDESC         object         4904\n",
       "COLLISIONTYPE      object         4904\n",
       "UNDERINFL          object         4884\n",
       "LOCATION           object         2677\n",
       "ADDRTYPE           object         1926\n",
       "ST_COLCODE         object           18\n",
       "SDOT_COLDESC       object            0\n",
       "SEGLANEKEY          int64            0\n",
       "CROSSWALKKEY        int64            0\n",
       "SEVERITYCODE        int64            0\n",
       "VEHCOUNT            int64            0\n",
       "SDOT_COLCODE        int64            0\n",
       "INCDTTM            object            0\n",
       "INCDATE            object            0\n",
       "PEDCYLCOUNT         int64            0\n",
       "PEDCOUNT            int64            0\n",
       "PERSONCOUNT         int64            0\n",
       "SEVERITYDESC       object            0\n",
       "SEVERITYCODE.1      int64            0\n",
       "STATUS             object            0\n",
       "REPORTNO           object            0\n",
       "COLDETKEY           int64            0\n",
       "INCKEY              int64            0\n",
       "OBJECTID            int64            0\n",
       "HITPARKEDCAR       object            0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([data.dtypes, data.isna().sum()], axis=1, \n",
    "          keys=[\"Data Types\", \"Null Values\"]).sort_values(by=[\"Null Values\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Data Types</th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SDOTCOLNUM</th>\n",
       "      <td>float64</td>\n",
       "      <td>79737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>6329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>float64</td>\n",
       "      <td>5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>float64</td>\n",
       "      <td>5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER</th>\n",
       "      <td>object</td>\n",
       "      <td>5081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROADCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNDERINFL</th>\n",
       "      <td>object</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCATION</th>\n",
       "      <td>object</td>\n",
       "      <td>2677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADDRTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <td>object</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCDTTM</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCDATE</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYDESC</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE.1</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUS</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPORTNO</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLDETKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBJECTID</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Data Types  Null Values\n",
       "SDOTCOLNUM        float64        79737\n",
       "JUNCTIONTYPE       object         6329\n",
       "Y                 float64         5334\n",
       "X                 float64         5334\n",
       "LIGHTCOND          object         5170\n",
       "WEATHER            object         5081\n",
       "ROADCOND           object         5012\n",
       "ST_COLDESC         object         4904\n",
       "COLLISIONTYPE      object         4904\n",
       "UNDERINFL          object         4884\n",
       "LOCATION           object         2677\n",
       "ADDRTYPE           object         1926\n",
       "ST_COLCODE         object           18\n",
       "SDOT_COLDESC       object            0\n",
       "SEVERITYCODE        int64            0\n",
       "SEGLANEKEY          int64            0\n",
       "CROSSWALKKEY        int64            0\n",
       "SDOT_COLCODE        int64            0\n",
       "VEHCOUNT            int64            0\n",
       "INCDTTM            object            0\n",
       "INCDATE            object            0\n",
       "PEDCYLCOUNT         int64            0\n",
       "PEDCOUNT            int64            0\n",
       "PERSONCOUNT         int64            0\n",
       "SEVERITYDESC       object            0\n",
       "SEVERITYCODE.1      int64            0\n",
       "STATUS             object            0\n",
       "REPORTNO           object            0\n",
       "COLDETKEY           int64            0\n",
       "INCKEY              int64            0\n",
       "OBJECTID            int64            0\n",
       "HITPARKEDCAR       object            0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping features with more than 50% missing values\n",
    "data.drop(columns=[\"PEDROWNOTGRNT\", \"EXCEPTRSNDESC\", \"SPEEDING\", \"INATTENTIONIND\", \"INTKEY\", \"EXCEPTRSNCODE\"], inplace=True)\n",
    "pd.concat([data.dtypes, data.isna().sum()], axis=1, \n",
    "          keys=[\"Data Types\", \"Null Values\"]).sort_values(by=[\"Null Values\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194673 rows matched.\n",
      "194673 rows matched.\n"
     ]
    }
   ],
   "source": [
    "# Checking the severitycode and its related columns\n",
    "print(sum(data[\"SEVERITYCODE\"] == data[\"SEVERITYCODE.1\"]), \"rows matched.\")\n",
    "ref = {\"Property Damage Only Collision\":1, \"Injury Collision\":2}\n",
    "print(sum(data[\"SEVERITYCODE\"] == data[\"SEVERITYDESC\"].apply(lambda x: ref[x])), \"rows matched.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"JUNCTIONTYPE\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"LIGHTCOND\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"WEATHER\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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/ug0GQ5ldSCQiIvkVehXowoUL8/w3ffp0jEYj9erVIyIiwuLhnJ2dmT17NvHx8UBu2Zw8eZLGjRsXuL6rqyt16tRh165dABw/fpyrV6/i6emJr68v0dHR2NnZ4ebmRuPGjVmxYgUdOnQgOzubK1eumB/xlpCQwPz588nMzATgypUrXL9+nccffzzP/tzd3fnpp58A2LNnD5BbhF26dMHd3Z2//e1v9O7dm5MnT5b+L0dEREqkyG+D2LdvH7179+aRRx5h/fr1+crAEry9vRkzZgwjRoyga9eudOvWDXt7e0aPHn3bbcLCwli5ciW9evXin//8J+Hh4Tg6OuLq6sqDDz5Iy5YtzWNXrlyZRx55hGPHjtG8eXPzGH//+98xmUx069aNHj168PLLL/Pmm2/SoEGDPPt66aWXWL16NX369DFfqOPg4MDYsWMZNmwYQUFBfPvtt7z88ssW+O2IiEhJGEx3mIfLzMwkNDSU7du3895775mnDMU6yts5QA8Pt3KXCZSrOMpjJlCu4lKu/OzsDHneovNnhZ4D/Omnn3j99dfx8PBg06ZNVK9evdQDioiIWEOhBRgcHIzJZKJWrVpMnjw53+cLFy60WDARERFLKrQA33333TKKISIiUrYKLcA+ffoAYDQaOXPmDACPPvqonl4iIiIV3h3fCB8REcHs2bOB3CKsVKkSr776KgMHDrR4OMkrKzPD2hFERO4ZhRbgnj17WLlyJcuWLTPfexcbG8s777xD9erV6dy5c5mElFzJKXr8mYhIaSn0PsClS5cyd+7cPDeeN2vWjLlz57J06VKLhxMREbGUQgswLS2NRx99NN/yBg0acOPGDYuFEhERsbQ7FuDtZGdnl3oYERGRslJoAdavX58vv/wy3/Ivv/yywCNDsaxq1Vzx8HDDw8MNtyrO1o4jIlKhFXoRzNixYxk5ciSjRo3imWeewWg0EhMTw5IlS/jPf/5TVhnl/4z9YCNXE1MBWD1zMCmkWzmRiEjFVWgBNmnShPDwcObOnUtoaCgALVu2ZPHixXh6epZJQBEREUu4432AzZo145NPPimLLCIiImXmjq9DWr9+PbGxseafw8LC2LBhg0VDiYiIWFqhBbhu3ToWLVpEpUqVzMtatGjBggUL2Lhxo6WziYiIWEyhBbh69eo8T4EB8Pf355NPPmHFihUWD1fRNGzY0NoRRESkiAotQJPJRO3atfMtr1u3ru4DFBGRCq3QAszOziYnJyff8pycHLKysiwWqqIzmUyEhYXRtWtXAgICWL58OQA///wzL7zwAn369GHgwIF8//33Vk4qImK7Ci3A1q1bs2zZsnzLly5dypNPPmmpTBXejh07OHr0KJs3b2bt2rVERkZy5coV3n77bcaPH8+GDRuYNm0ar732mrWjiojYrEJvgxg3bhx//etf2bNnDy1atCAnJ4fjx49z8+bNAotRcn3zzTd0794dR0dHHB0diYqKIjU1lbi4OCZOnGheLy0tjcTERNzd3Ys07kfje2DnkHtBUnqG3gwhIlIShRagm5sba9euZevWrXz33XcYDAYGDx5Mly5d8lwZKnk5ODhgMBjMP8fHx3P//feby/CWS5cuUbVq1SKPa+dQiStXUkozqoiIzbrjfYCOjo706dOH0aNH8/rrr9OjRw+V3x20atWKXbt2YTQa+f3333nppZe4evUqjzzyiLkADxw4wODBg62cVETEdt3xSTDLli1jyZIlXLt2DYA6derw4osvMmDAAIuHq6g6d+5MXFwcQUFB5OTkMGTIEOrXr09YWBjvvvsuS5YsoVKlSsyZMyfPkaKIiJSdQgtw9erVbN++nfDwcBo1aoTBYCA2NpbZs2cDqAT/5McffzT/+bXXXst3kUuDBg1YuXJlWccSEZECFDoFun79ehYtWsTTTz/Nfffdh7OzM61bt2bevHmsW7eurDKKiIiUukILMCsrq8CLNGrWrFng/YEiIiIVRaEFaGd3+49NJlOphxERESkrhZ4DzM7OJjk5ucCy06PQRESkIiu0AE+dOoW3t3eBBairF8teVmaGtSOIiNwzCi3AH3744baf6VmgZS85RU9/EREpLXe8Ef7PkpOTWbx4Mf7+/pbIIyIiUibueCP8LWfOnGHFihVs2rSJ6tWr88orr1gyl4iIiEXdsQCjo6NZtmwZX3/9NW3atKFy5crs2LEDe3v7ssgnIiJiEYUWYM+ePalUqRKBgYHMmDGD6tWr4+/vr/KzEldXJ27cSLd2DBGRe0Kh5wAdHR3JysoiMTGR5OTkssokt+HkpIeQi4iUlkKPACMjI4mNjeW///0vQUFBPPbYY6SmppKamoqLi0tZZRQRESl1d7wKtFmzZnzwwQfs27ePHj16cP/999OhQwdmzZpVFvlEREQsosi3QVStWpVhw4axc+dO5syZw/nz5y0YS0RExLLueBVoTk4Ou3fv5siRIxgMBlq0aEGnTp1o27ZtWeQTERGxiEKPADMyMhgyZAgLFizA3t6erKws5s+fz1//+lfS0+/9qxFTU1OZOnUqnTt3JjAwkEGDBnHw4EEAIiIi2LJlCwATJkwgMjLSmlFFRKSYCj0CXLBgAY0bN2bSpEnmZSaTiWnTpjF//nzeeOMNiwe0FpPJxIgRI2jcuDFbt27F0dGR77//nuHDhzN79myOHj1K69atrR1TRETuUqEF+PnnnxMREZFnmcFg4K233iI4OPieLsBDhw5x8eJFVqxYYX7wd5MmTRg5ciTPP/88bm5ufP3113h4eADwxRdfsHr1aq5du8aIESPo378/qamp/POf/+T06dNkZ2fz8ssv07NnTyIjI9mwYQNJSUl07NiR119/3ZpfVUTEJhVagCaTCScnp3zLnZ2dC31X4L3gxIkTNG3aNN9bL1q1asV9992Hn58frVu3pl27dmzdupXMzEzWrl3L6dOnGTJkCP3792fBggU88cQThIaGcvPmTQYMGEDz5s0BSEhIYNu2bTg4FPlpdCIiUooK/ds3IyODjIyMfCWYkZFxz78R3mAwFPjOQ6PRWOCroPz9/TEYDHh6epKYmAjAV199RXp6OuvXrwcgLS2N06dPA7lHk8Utv2xjZnG/hoiI3Eahh3F+fn589NFH+ZbPmjWLTp06WSpTudC8eXPi4uIwGvO+guj48eM0bdo03/q3Hg/3x3LMyckhLCyMqKgooqKiiIiIoF27dkDuUXRx2VdyLPY2IiJSsEILcOzYsRw7doz+/fsTFhbGBx98QFBQECdPnmT06NFlldEqnnnmGR577DHef/99cwnGxcWxYMECRo0ahb29fYFHiH/k7e3Nf//7XwAuX75MYGAgv/32m8Wzi4jInRU6B3ffffexatUqtm/fzvHjxwF46aWX6Nq1q008EPvjjz9mzpw59OzZE3t7e+6//37CwsLw8vLi6tWrfPjhh7i5ud12+zFjxvDuu+/Ss2dPsrOzGT9+PA8//DCHDx8uw28hIiIFMZhMJtPdbHjgwAF8fX1LO4/cwZUrKdaOkIeHh1u5ywTKVRzlMRMoV3EpV352dgaqVXO9/eeFbRwXF8eAAQMYMWIE169fB+DixYuMGTOGkSNHlm5SERGRMlRoAU6dOpUuXbpQp04dFixYwJ49ewgMDOT3338nKiqqrDKKiIiUukLPAaakpDBs2DCys7Pp2rUr27dvZ+rUqfTo0aOs8omIiFjEHS+CgdxL/DMyMli8eDFNmjQpk2AiIiKWVOgU6B+vj3F3d1f5WVlWZoa1I4iI3DMKPQLMyckhOTnZXIR//DPkviNQyk5yivHOK4mISJEUWoCnTp3C29vbXHpeXl7mzwwGAydPnrRsOhEREQsptAB/+OGHssohIiJSpu7tVzqIiIjchgqwAnF1zf9qKhERuTsqwArEyamStSOIiNwzVIAiImKTVIAiImKTVIAiImKTVIAiImKTyrwAd+zYQVBQEIGBgfTq1YslS5aUyrgNGzY0//m3335j3LhxREZG0rBhQ7Zs2ZJn3WXLltGwYUPi4+OLPH54eDjh4eHmn9955x1++ukn/Pz8CAgIoHfv3gQEBPDCCy8QFxdnXq93794FjhcREZEvl4iIlJ1Cb4QvbQkJCYSGhhIZGYm7uzupqamEhIRQv359/P39S20/X375Jc8++ywADz74IDt37qRnz57mz3fv3k2VKlVKtI8zZ87w2GOPAbB48WLq1KkDwBdffMGLL77I9u3beeCBB2772qijR4/SunXrEmUQEZG7V6ZHgImJiRiNRtLT0wFwcXFhxowZPPbYY2zfvp1+/foRGBhIt27dOHr0KAAhISHMnDmT/v3707lzZ/bt2wdAfHw8AwcOpHfv3kyZMiXPfvbv30+7du0AaNWqFXFxcaSlpQG5L/R1cXHBzc0NgJiYGEJCQszbTpgwgcjISACWLFlCly5d6N+/P7GxseZ1fvjhhzxHnH/UoUMHmjVrZj66u7VeeHg4L774IgEBAaxcuZK9e/cyb9489u/fX4LfqIiI3K0yLcBGjRrh7+9Pp06d6Nu3L2FhYeTk5FC3bl0+/fRTFi5cyKZNm3jppZdYvHixeTuj0ciaNWuYOHEic+fOBWDatGkEBQURFRVFixYt8qyblJREjRo1AHBwcKBt27bm4ty2bRvdu3e/Y9YTJ06wfv16NmzYwNKlS7l06ZL5sz8eYRbE09OTs2fP5luemZnJtm3bCAkJwc/Pj7Fjx5qLWkREylaZnwOcOnUqe/fuZeDAgVy8eJF+/fqxZ88e5s+fT3R0NHPnzmXDhg2kpqaat7lVEp6eniQlJQFw6NAhc5EFBgZSqVLuTeJHjhzJU4gA3bt3Z+fOnQDs2bOHTp063THnoUOHaN++PS4uLlSuXJlu3bqZPzt48CDe3t633dZgMODs7JxvebNmze6438JkGzNLtL2IiPy/Mi3AL774gm3btlGzZk2Cg4OZM2cOkydPZtWqVfTt25f4+HhatWqVZ0oSwMkp9xFgBoMhz/Jbb6kwGAzY2eV+lYKOzry8vDhx4gSnTp3C3d3dPP15a9s/vuLJaDQWuNzBIfd06c2bN7Gzs8PV1fW23/PHH3+kQYMG+ZYXVIrFYV/JsUTbi4jI/yvTAnR2dmb27Nnmqy9NJhMnT57E0dERg8HAiBEj8PLyYvfu3WRnZxc6Vps2bdi0aRMAu3btIiMj92Wxx44d46mnnsqzrr29Pb6+vkyZMoWAgIA8n7m7u3PhwgUyMjJISkriyJEjAPj4+PD555+TkpJCRkYGu3fvBuDAgQO0adPmtrn27t3LyZMn7zjNam9vf8fvKCIillOmV4F6e3szZswYRowYYT7SateuHfPnz2fChAl0794dg8FA27ZtzUV0O1OmTGH8+PGsWbOGpk2b4uLiwqVLl/Dw8DAfrf1R9+7diYqKws/PL89yT09P2rdvT48ePXjooYdo2bIlAI0bN2bo0KH07duXKlWqULt2bSD3CHPo0KF5xhg+fLh5Ctbd3Z1PPvmk0CNEyC3wDz/8EDc3tzzTqyIiUjYMpj/O80m5d+VKirUj5OHh4VbuMoFyFUd5zATKVVzKlZ+dnYFq1W5/MKInwYiIiE1SAYqIiE1SAYqIiE1SAYqIiE1SAYqIiE1SAVYgWZkZ1o4gInLPUAFWIMkpRmtHEBG5Z6gARUTEJqkARUTEJqkAKxBXVydrRxARuWeoACsQJ6dK1o4gInLPUAGKiIhNUgGKiIhNUgGKiIhNUgGKiIhNslgB7tixg6CgIAIDA+nVqxdLliwxfzZv3jwOHz5cKvvx8/MjPj6ezz77jLlz55ZorISEBFq3bs2tVySaTCZ8fHyYPHmyeZ39+/cTEhJy2zFiY2MJCwsrUQ4REbE8i7wRPiEhgdDQUCIjI3F3dyc1NZWQkBDq16+Pv78/33zzDV5eXqW6T39/f/z9/Us0Rs2aNXF3d+enn37C09OT7777joYNG3Lw4EHzOocPH6ZNmza3HeOnn37i2rVrJcohImJN2dlZJCZeISsrs8RjXb5sR05OTimkKpyDgyPu7h7Y2xe91ixSgImJiRiNRtLT0wFwcXFhxowZODk5sXHjRuLi4pg8eTIff/wxycnJzJkzh/T0dG7cuMHEiRPp1KkTEyZMwNXVle+++46EhARGjx5NcHAwSUlJjB8/nkuXLtGgQQMyMnKfjxkZGcmhQ4eYMWMGfn5+BAYGEh0dze+//05oaChNmzbl1KlTTJgwgezsbJ555hm+/PJLdu/enSe7j48PR48exdPTk+joaDp37symTZs4c+YMDRo04MiRI7z11lucOnWKadOmkZaWxvXr1xk+fDg9evRg3rx5pKWlsWDBAoYPH87MmTM5dOgQ2dnZBAUF8fzzzxMTE0NYWBg5OTl4enoSGhpqif8NIiJ3JTHxCs7OlXFxeRCDwVCisRwc7MjKsmwBmkwmUlNvkJh4herVaxV5O4tMgTZq1Ah/f386depE3759zX/Z16tXj+eee46mTZsyffp0GjZsyP/8z/8wffp0NmzYwPTp0/NMY166dInVq1ezYMECZs6cCeROnzZp0oTNmzczePBgrl69WmCGqlWrsm7dOgYMGMCiRYsAmDBhAuPGjSMqKoq6deuSnZ2dbztvb2+OHj0KQHR0NO3atcPX15f9+/eTmZnJzz//TNOmTVm7di2jRo1i/fr1rFixgpkzZ1KlShXGjh2Ln58fI0eOJCIiAoANGzawbt06PvvsM/PU7/nz51m+fLnKT0TKnaysTFxcqpS4/MqKwWDAxaVKsY9YLXYOcOrUqezdu5eBAwdy8eJF+vXrx65du/KtFxYWxunTp5k/fz5Lly4lNTXV/Jmvry8Gg4HHH3+cpKQkAA4dOkRAQAAArVq1om7dugXuv127dgB4enqSlJREUlISv/76K+3btwcgODi4wO28vLw4fvw4N2/e5OrVqzz88MO0adOGQ4cOERsbS4sWLbCzs2PChAlkZGSwaNEiPvroI9LS0vKNdfDgQfbu3Uvv3r35y1/+wqVLl/jxxx8BqF+/Pm5ubkX8bebKNpZ8OkJEpCgqSvndcjd5LTIF+sUXX5CWlkZAQADBwcEEBwcTERHBunXr6NKlS551Bw0ahJeXF15eXvj4+PDmm2+aP3Nyyn301x+/mMFgMF+kAmBvb19ghj9va29vn2e723F3d6dy5cps377dfJ7yqaee4syZMxw5cgRfX18AXn31VapUqULHjh0JCAhgy5Yt+cbKzs5m/Pjx5u98/fp1XFxcOH78OM7OznfM8mf2lRwBvRJJRMqWWxVnnC3wJKr0DCMpN9KLtG5q6k1GjBjGzJkfUatW7VLZv0UK0NnZmWnTptGsWTPq1KmDyWTi5MmTNG7cGMgto+zsbJKSkjh//jyrV6/G0dGRWbNmFTgt+Uc+Pj5ERUXRqFEjYmNj+eWXX4qUyc3Njbp167Jv3z7at2/P5s2bb7uut7c3y5Yt49VXXwXAwcGBRx99lC1btrBw4UIADhw4wPbt26lZsyarVq0CcgvP3t6erKws8zgRERF07NiRzMxMBg0axNSpU4uUV0SkvHB2qsSgt1aV+rirZw4mhTsX4HffxTFz5nQuXCja3/dFZZEpUG9vb8aMGcOIESPo2rUr3bp1w97entGjRwO505P/+Mc/OHv2LH379qVHjx50796d1NRU0tPTC5xOvGXs2LFcuHCBHj168O9///u2U6AFmTlzJv/617/o06cPsbGxtz0K8/b25vz583h7e5uXtW3blszMTB566CEAXnnlFQYNGkRAQABHjhzhoYceIj4+nmbNmvHtt98ya9YsBgwYwCOPPEKfPn0IDg4mKCio1K9+FRG5123evIHXX3+b6tU9SnVcg6ko84L3iI8//ph+/fpRo0YNdu3axebNmwkPD7d2rGK5ciXF2hHy8PBwK3eZQLmKozxmAuUqrtLMdenSzzz4YL08Y1vqCLA4mfv27UV4+KLbToH+ObednYFq1VxvO55FpkDLq9q1azNs2DAcHByoUqUK7733nrUjiYiIldhUAQYFBREUFGTtGCIiUg7oWaAiImKTVIAiImKTbGoKVEREii89w8jqmYMtMm5xrFt3+9vX7oYKsALJytRN8CJS9lJupBfpfr2ClMWzQO+WpkArkOSU4v1rSUREbk8FKCIiNkkFKCIiNkkFKCIiNkkFWIG4ujpZO4KIyD1DBViBOFngdSQiIrZKt0GIiEih3O93xMGx9GegsjIzSEwu2ou+//OfxezduweANm18GTVqXIn3rwIUEZFCOTg6cWTmS6U+bsu3lgB3LsBvvonhm2++ZunSVRgMBt544xX27fuc9u07lmj/KkARESnXqlWrzujRr1GpUu5poHr1HiEh4VKJx9U5QBERKdcefbQBTZs+CcCFC7+wd+8efHx8SzyuCtACUlNTmTp1Kp07dyYwMJBBgwZx8OBBACZNmsSJEyeIiYkhJCTEyklFRCqOs2fP8Nproxk9ehx16z5c4vE0BVrKTCYTI0aMoHHjxmzduhVHR0e+//57hg8fzuzZs80v4Y2JibFyUhGRiiM29jiTJ7/N2LGv06lT11IZU0eApezQoUNcvHiRiRMn4ujoCECTJk0YOXIk//rXvwgJCVH5iYgUQ0LCJd55503+8Y/ppVZ+oCPAUnfixAmaNm2KwWDIs7xVq1bMnj2bJ5544q7HzjYW7XJhEZHSlJWZ8X9XbJb+uEXx3//+DxkZmYSHzzEve+65IJ57rm+J9q8CLGUGg4Hs7Ox8y41GY75SLC77So6AXokkImUr9169u/sHeGm8DunVV9/k1VffLNEYBdEUaClr3rw5cXFxGI15X110/PhxmjZtaqVUIiLyZyrAUvbMM8/w2GOP8f7775tLMC4ujgULFjBq1CgrpxMRkVtUgBbw8ccf4+joSM+ePQkICOC9994jLCwMLy8va0cTEZH/o3OAFuDs7MzEiROZOHFivs9Wrlxp/rMKUUTKK5PJVOLrFsqSyWQq9jY6AhQRkTwcHBxJTb1xV6ViDSaTidTUGzg4OBZrOx0BiohIHu7uHiQmXuHmzaQSj2VnZ0dOTsmuAi0KBwdH3N09ireNhbKIiEgFZW/vQPXqtUplLA8PN65cSSmVsUqbpkBFRMQmqQArkKI+NUFERO5MU6AVSEpqFnZ25e+qrPKYCZSrOMpjJlCu4lKu4u3XYKool/mIiIiUIk2BioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBioiITVIBikiZGzZsGNevX7d2DLFxKkARKXMHDhywdgQRFaCI5LVu3Tp69OhBr169GDJkCL/99htr1qyhZ8+eBAYGMmzYMM6dOwfAhAkT+OSTT8zb/vFnPz8/wsPDGTRoEB07duSjjz4CYOLEiQAMHTqU3377rWy/nMgf6I3wImL2ww8/MGvWLDZs2ECtWrVYtmwZzz//PDk5OaxZs4YHHniAyMhIRo8ezdatW+84XlpaGqtXryYhIYHOnTsTHBzMBx98QGRkJMuXL+eBBx4og28lUjAdAYqI2cGDB2nbti21atUC4Pnnn8ff35+AgABzWQUFBZGQkEB8fPwdx/P39wegZs2aVKtWjeTkZMuFFykmFaCImNnb22MwGMw/p6enc+HChXzrmUwmsrKyMBgMmEwm83Kj0ZhnPScnJ/Of/7yuiLWpAEXEzMvLi4MHD3L58mUAPv30U/bt28e2bdvMV22uX7+eqlWrUq9ePdzd3YmLiwMgISGBQ4cOFWk/9vb2ZGVlWeZLiBSRzgGKiFnDhg0ZP348L730EgAeHh7s3r2bPXv2MHToUHJycnjggQdYtGgRdnZ2hISE8Oabb9K1a1fq1KmDt7d3kfbTrVs3QkJCCA8P5/HHH7fkVxK5LYNJcxIiImKDNAUqIiI2SQUoIiI2SQUoIiI2SQUoIiI2SQUoIiI2SQUoIiI2SQUoIiI2SQUoIiI26X8BiHWq0k+1uCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"ROADCOND\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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OTZkyxXZObi0lJQ2LRWNjRESKg91uwxsHIt48V0qDFEVExFnsJq8bV54oyjkREZGSZLfbUK2r4mNvvsLdrlq1is4OoVhlZuVw9Uqms8MQKdPsJi+1rorPhMhYklPTnR2GOEB01FCuouQlUpLsJq8TJ07QvHlzADIzM20/W61WsrOzSz46ERGRfNhNXrt27XJUHCIiIoVmN3nVrFmTn376iTNnztC0aVPuv/9+R8UlIiJSILujDTdt2sRf/vIX3nnnHXr37s2XX37pqLjuWHp6Oq+99hp//vOf6d27N0OGDGHfvn0AbNiwgQ8//BCAsLAwNm/e7MxQRUSkiOy2vNasWcO2bdu4//77+e6775g/fz5t27Z1VGy3zWq1MnbsWBo0aMD27dspV64cx44dY8yYMcydO5dvv/2Wli1bOjtMERG5Tbfcz+t6V2GzZs1ITU0t8YCKw4EDB/h//+//8f7779tGTDZs2JBx48YxYsQIKlasyP79+6lWrRoAn3/+OdHR0aSkpDB27FgGDhxIeno6r7/+OgkJCZjNZkaPHk3Pnj3ZvHkzW7Zs4fLly3Ts2JGXXnrJma8qInJXKtJQeaPRWKLBFJcjR47g5+eXJ/4nn3ySe+65h06dOtGyZUvatWvH9u3byc7OZuPGjSQkJDBs2DAGDhzI0qVLadSoEbNmzSItLY1BgwbRtGlTAJKSkvjoo48KXPdRRERKVpH++rrKvC+DwYDZbM5zPCcnJ9936Ny5MwaDgXr16tlal19//TWZmZls2rQJgIyMDBISEoBrrbiiJq6FU/oU8S2cw2LKwc3dw9lhuLTMrBxnhyBS5tn9C3z8+HHb3C74Y66X1WrFYDDkWe+wtGjatClr1qwhJycHD48//hB///33+Pn55bn+eovyxsRmsViYPXs2jRo1AiA5OZlKlSqxbds2vLy8ihxTce2kXNIcvVNzWdv9VUQcw27y+uSTTxwVR7Fq0aIFdevW5Z///Cf/+Mc/8PDw4OjRoyxdupS5c+eydevWfFtmNwoICOCDDz5gxowZXLhwgT59+rBu3ToHvYGIiNhjN3nFxMQwYcIEl+kuvNHixYuZP38+PXv2xGg0UqlSJWbPno2/vz/JycnMmzePihULXlNv/PjxRERE0LNnT8xmM6GhoTz00EN88803DnwLERHJj8FqZ/XdwYMH4+7uzrx582wj8+T2qNswf+o2zJ/qJX+ql4KVtbpxczPYXdDc7iTltWvX0qpVK/r16+dSE5RFRKRss9tt6ObmxgsvvECHDh0IDw/n008/5aGHHrKdHzlyZIkHKCIicrNCjfd2c3PDYDBw8uRJMjO11YOIiDjXLTejXLZsGStXruTFF19k6NChjopLRESkQHaT16BBg/j999+Jjo7m8ccfd1RMZVLjsbOcHUKhmLKznB2CiMgt2U1ejRo1IiwsjHLlyjkqnjIrJSUNi6XAgZ0iIlIEdkcbXr16VYlLRERKHbvJ6+TJk46KQ0REpNC0LLqD3DzZLjMrh6tXNHJTROR22E1eJ06cyLUw73WlfWHe0mhCZCzJqem236OjhnIVJS8RkdthN3nVqVOH5cuXOyoWERGRQrGbvMqVK0fNmjUdFYuIiEih2B2w4enp6ag4RERECs1u8nKF/avS0tJ47bXX6NmzJ4GBgQQHB/PDDz8UqYz4+HiCg4NLKEIRESludrsNg4ODC9zLy2AwsHr16hIJqrAsFgujR4/G39+f2NhY3N3d2b9/P6NHj2b79u34+Pg4NT4RESkZdpPXX/7ylzzHTp8+zZIlS2jatGmJBVVY8fHxnD9/ngkTJuDmdq0RGRAQQGRkJKGhoTz99NMMGDAAuJaIJ02aRLly5Zg+fTqZmZlUqlSJOXPm5Crz7NmzREREcPnyZby8vJg2bRoNGzZk27ZtrFixAqPRSK1atZg9e7a6VUVEnMRu8uratWuu3zdu3MjKlSsZN24cY8eOLdHACuPYsWPUr1/flriua9++PZ6enixatIgBAwZw7tw5Ll26RNOmTenRoweTJk2iY8eOREdHs3r1ajp06GC795VXXmH69Ok0bNiQkydPEhISws6dO1mwYAEbNmygSpUqzJo1i1OnTtGgQQMHv7GIiEAhJylfvXqV8PBwjh07xooVK0pFqwuubdVSUOvH39+fadOmkZiYSFxcHIGBgVy6dImLFy/SsWNHAIYMGQJca8EBpKenc/ToUaZMmWIrJyMjg9TUVDp27MjgwYPp0qULXbt2LXLiWjilT55j1apVLFIZd8qUnUXqb9kOfaaISEm4ZfL65ptvCA0NpUWLFmzZsoUKFQreltnR/Pz8iI6Otk2avm7evHm0bt2aPn36sH37dnbs2MHKlSvx8PDIdV1WVhYXLlyw/W6xWChXrhxxcXG2Y7/++iuVK1cmPDyc48ePs2fPHkJDQxk/fjyBgYGFjvXIslfIvpJyh298Z56YvAJQ8hIR12d3tOHChQsZPXo0I0aMYOrUqZhMJi5fvmz7x9latGhBlSpVWLx4MWazGYAvvviCzZs3U7duXYKCgli3bh0PPvgg999/PxUrVuT+++/nyy+/BCAuLo633nrLVl7FihV55JFHbMnrq6++YujQoZhMJp5++ml8fHz461//SmBgID/++KPjX1hERIBbtLyWLFkCQGRkJDNnzsRq/WNLD4PB4PQ/4AaDgSVLlhAZGUnPnj1xd3fHx8eH5cuXU7VqVQAefPBB+vbta7tn9uzZREREMHv2bHx8fIiKiuL06dN5zq9YsQIPDw/mz5+Ph4cHEyZMYNSoUXh6elKlShVmzpzp8PcVEZFrDNYbM1IZYrVauXDhAsHBwXz44YdO39qltHQbXrx41akx3KxatYqlLqbSQPWSP9VLwcpa3bi5GfIsaH6jW37zslgs7Nq1i0OHDmEwGGjevDldunTBaDQWa6DFbefOnURERBAREeH0xCUiIsXL7jevrKwshg0bxtKlSzEajZhMJv71r3/xl7/8hczM0r0ierdu3di/fz/dunVzdigiIlLM7La8li5dSoMGDZg6dartmNVq5Y033uBf//oXL7/8cokHKCIicjO737wCAwPZsGFDnrlUmZmZ9OvXj+3bt5d4gFJ8SuM8r7LWT19cVC/5U70UrKzVzR1987JarflOAvby8sqzqoXYl5KShsVSJsfGiIg43C2/eWVlZeV73GKxlFhQIiIi9thNXp06dWLBggV5js+ZM4cuXbqUVEwiIiJ22e02nDBhAiNHjmTgwIG0aNECk8nEwYMHuffee3n33XcdFaOIiEgut5ykbDab2bFjB99//z0AzZs3p2vXrqV+npeIK8nMyuHqlT+mn5S1j+/FRfVSsLJWN7casFFmV9gobSZExpKcmu7sMKSUio4amusPT1n7Q1RcVC8FK2t1c0ejDW+1Z9eyZctuLyoREZE7UKTNKEVEREoDu8nrxtXYRURESotbLsy7ceNGmjVrRt26dfnggw+Ijo6mUaNGTJ8+nXvvvTffexITE+nWrRuPPfYYcG1x3/T0dPr06cOECROK9QVuftZ1AwYMYOjQoYUqIz4+nsWLF7NmzZpCP9fX15cTJ04UKVYRESkedpPX22+/zc6dO2nevDk//vgjM2fO5I033iAhIYHZs2fz6quvFnhv9erVc+1InJSURNeuXenRo0eeRHOnbn6WiIiUbXaT10cffcTatWupUKEC8+fPp3379vTu3Rur1cozzzxTpAddvHgRq9VK+fLlAVi+fDk7duzAbDbTtm1bQkNDMRgMzJ8/n3379vHbb79RvXp15s+fT9WqVQkICMDPz4+LFy8SExODh4dHoZ7btm1bunbtyqFDhzAajSxYsIDatWvz5ZdfEhkZiaenJ3Xq1LFdf/bsWSIiIrh8+TJeXl5MmzaNhg0bkpiYSGhoKBkZGTRt2rRI7y4iIsXrlt2GFSpcG6r47bff0r17d+DaDsbu7vZvvXDhAoGBgWRlZZGamkrjxo1ZvHgxDzzwAHv37uXo0aPExMRgMBgIDQ1l69at/OlPf+LUqVOsW7cONzc3Jk+ezNatWxk1ahSpqamMHj0af3//Ap91o6ioKHx9fbl48SKtWrVi2rRpzJw5k7Vr1/LSSy8RFhbG6tWreeyxx3Ktmv/KK68wffp0GjZsyMmTJwkJCWHnzp288cYbBAUF0b9/f2JjY1m/fv2ta/cGC6f0KdL14jgWUw5u7oX7n6GSkpmV49Tni7iaWy7Ma7VayczM5MiRI0RERADXVpXPb83DG13vyrNYLMycOZOff/6ZNm3aALBv3z4OHz5MUFCQrbwaNWoQGBjIK6+8wsaNGzl9+jTff/89Dz30kK3Mglo8t+o2bNeuHQD16tXjm2++4cSJE1SvXt3Wfdm3b1/eeust0tPTOXr0KFOmTLHdm5GRQWpqKgcOHGDu3LkA9O7dm/DwcLvvf7PSsJOy5K807jAtIvbZTV7t2rXj73//OyaTiccee4zHHnuMM2fOsHDhQjp27FioB1xvQfXp04eVK1cyevRozGYzw4cPZ+TIkQBcuXIFo9HI0aNHefnllxkxYgRdu3bFzc2NG+dQe3l53dZLXl8Z32AwYLVabf++7vpqIRaLhXLlyuVKhL/++iuVK1cGsN1jMBi0qr6IiBPZ/Qv84osv0qRJE2rXrs2SJUuAa6MPPT09eemllwr9EHd3dyZPnsySJUu4ePEiAQEBxMXFkZ6ejslksnXNHTx4kJYtWzJ48GAeeeQRPv/8c8xm8529YT58fX1JTk7m+PHjALZ9ySpWrMgjjzxiS15fffWVbcRi69at2bp1KwAff/zxLVueIiJScuy2vNzd3XnuuedyHQsNDb2tBz311FM0a9aMt956ixkzZnD8+HEGDBiA2WymXbt29O3blwsXLjB+/Hh69eoFgJ+fH4mJibcsO79vXk8++WSBXXseHh7MmzeP0NBQ3N3dadiwoe3c7NmziYiIYMWKFXh4eDB//nwMBgPTp08nNDSU9evX4+fnZxt4IiIijmd3bcNVq1bZvfl6t5/cmr55lV6l8ZtXWVunrrioXgpW1urmjtY2/Omnn4o9IBERkTtlN3lFRkY6Kg4REZFCu+0hc4MHDy7OOERERArtlpOUC3J9pJ4UTuOxs5wdghTAlK2RoyKu5raTl8FgKM44yryUlDQsFu37ebOy9pFZRBxDM21FRMTl2G15zZgxI9/jVquVnBytxSYiIs5hN3ldXxYpP3/961+LO5Yyzd58Bbi2MOvVK5kOikZExLXZTV7jx48v8FxCQkKxB1OWTYiMJTk1vcDz0VFDuYqSl4hIYdz2N6+BAwcWZxwiIiKFdtvJy86qUiIiIiXqtpOXhsqLiIizaKi8iIi4HLsDNpo1a5ZvC+v67solITExkWHDhvHZZ5/lOu7r68uJEyfuuPxFixYB8Le//S3Puffee4/169djNBoxGo0MGDDAtp+XiIiUHnaT14cffuioOJxu0aJFHDx4kDVr1lC1alUuXbrECy+8wOXLlwkJCXF2eCIicgO7yatmzZoAHD16lEOHDmEwGGjevDl+fn4OCS4/ZrOZqKgoDhw4gNlsJigoiBEjRmAymYiIiCAhIYHk5GR8fX2ZN28eXl5erFixgg0bNuDj44O3tzdNmjTJVebvv//OypUr+fDDD6latSoA9913HzNmzKB///6MGjWKFStWkJSUxNmzZzl37hz9+/dn3LhxzqgCEZG7nt3kZbFYCA0NZf/+/TzxxBNkZ2fz9ttv07JlS+bMmYPRaCyRoPLbGfm6DRs2ALBlyxays7N57rnn8PPzw2q14uHhwfr167FYLAwfPpw9e/ZQo0YNNm3axJYtWzAYDAwcODBP8kpISOCee+6hVq1auY7XrVuXcuXKcerUKQBOnDjB2rVruXr1Kl26dGHo0KF4e3sX6p0WTumT73GLKQc3dw8ys7RiiYhIYdlNXqtWrcJsNrN7927KlSsHQHp6OlOmTOHdd99l9OjRJRJU9erViYuLy3XM19cXgH379vHjjz+yf/9+ADIyMjhx4gRDhw6lcuXKrF27llOnTnHmzBkyMjI4cOAA7du3p3z58gB069YNi8WSq2yDwYDZbM43FpPJZPvu5+/vT7ly5ahSpQqVK1fm6tWrhU5eBe2kXBp38RURKe3sJq/t27ezevVqW+ICKF++PG+88QbBwcEllrzsMZvNhIaG8vTTTwNw6dIlypcvz6effsrChQsZNmwYQUFBpKamYrVaMRgMueakubu7k52dnavMunXrkpOTw6lTp3j00UdtxxMSErBYLNSpUwcAT09P27mbyxUREcexO1TeYrFQsWLFPMcrVarktD/cAQEBbNiwgZycHNLT0xkyZAjff/89+/bto3v37vTr1w9vb2/i4+Mxm820atWK3bt3c/XqVbKysti1a1eeMu+55x7GjRvH1KlTSUm51jpKSUlh2rRpPP/889xzzz2Ofk0REbHDbssrIyMDi8WCm1vuHGexWJy2qvygQYM4e/Ysffv2xWQyERQUhL+/P5UrV2bSpEls374dDw8PmjdvTmJiIv3792f48OE8++yzeHt7U6NGjXzLHTNmDBUrVmTEiBG2FtugQYM0VF5EpBQyWO00oaZNm8ajjz7KyJEjcx1/5513OH/+PNOnTy/xAMsKffPKnzajzJ/qJX+ql4KVtbpxczPY3Y3DbsvrxRdfZPDgwRw5coQWLVpgMpmIj4/n1KlTrF+/vtiDFRERKQy737zuu+8+YmJiqF+/Pnv37uXrr7+madOmxMTEFHqUnYiISHGzm7yys7OZMWMGderUYdmyZSxbtoxDhw4xY8YMTCaTo2IUERHJxW634cKFC0lLS6N58+a2Y6+//jqvvfYaixYt4sUXXyzxAMuKxmNn5XvclJ3l4EhERFyf3eT1+eefExMTg5eXl+3Y/fffT1RUFAMHDlTyKoKUlDQsFs0LExEpDna7DT08PHIlrusqVKiQa+KyiIiII9lNXm5ubqSlpeU5npaWpm9eIiLiNHa7DXv27El4eDj//Oc/uffee4FrE5fDw8NtyzNJ4dibr1CWZGblcPVKyez1JiJynd3kNXz4cF599VXatGlDvXr1sFgs/Pzzz/Tq1Ut7XBXRhMhYklPTnR1GiYuOGspVlLxEpGTZTV5ubm688cYbjB07lh9++AE3NzeaNGlC9erVHRWfiIhIHnaT13U1a9a0bUwpIiLibHYHbIiIiJRGpS55xcfHExwcXKR7Fi5cSIcOHVi1ahVTpkzh3Llzuc4vXbqUwMBAAgMD8fX1tf28dOnS4gxdREQcpFDdhqVdXFwcq1atok6dOnTq1CnPYJJx48Yxbtw44NqOzDfv0iwiIq6l1LW87Fm+fDl9+/ald+/eREVFYbVamT59OklJSYSEhLB8+XIuXLjAmDFjSE1NLVSZwcHBxMfHA5CYmEinTp0ACAsLY+zYsXTv3p3PPvuMTp06sWDBAp599ll69OjB0aNHS+w9RUTEPpdJXnv37uXo0aPExMQQGxtLUlISW7du5fXXX6d69eosX76cMWPG2H728fG542dWrlyZHTt22BJa5cqViYmJYdCgQbz99tt3XL6IiNwel+k23LdvH4cPHyYoKAiAzMzMAndFLi5NmjTJ9Xu7du0AqFevHh9//HGRylo4pU9xhVWsLKYc3Nw9iq28zCzn7LAtIncXl0leZrOZ4cOH23Z1vnLlCkajsVjKvr6Z9M1LXt28rqOnpycABoOhyM8oaCdlZ7vbd3IWEdfkMt2GAQEBxMXFkZ6ejslkIiQkhJ07d+a5zmg0YjabC12uj48PJ0+eBOCTTz4ptnhFRKTklMrk9c0339CsWTPbP9OnT6dTp048/fTTDBgwgJ49e1K/fn369u2b594OHTowZswYfvnll0I96/nnnyc6Opq+ffuSmalljUREXIHBer3PTEqUug3zV61aRXVb5kP1kj/VS8HKWt24uRnsLmheKlteIiIi9ih5iYiIy1HyEhERl6PkJSIiLsdl5nm5usZjZzk7hHyZsrOcHYKISJEpeTlISkoaFosGdoqIFAd1G4qIiMtR8hIREZejbkMHuXmyXWZWDlevaEUPEZHboeTlIBMiY0lOTbf9Hh01lKsoeYmI3A4lLxGRMsRsNpGaehGTKdvZoRSau3s5fHyqYTQWPiUpeYmIlCGpqRfx8rqX8uUfuK3tmxzNarWSnn6F1NSLVK36YKHv04ANEZEyxGTKpnx5b5dIXHBtf8Ty5b2L3FJ0ePJKTEzEz8+PwMBA+vTpQ48ePRg5ciS//vproe/v1KnTbT07ODg43+NHjhxh6tSpt1WmiEhp4yqJ67rbidcp3YbVq1cnLi7O9vvMmTOJiopi3rx5JfrcAwcO5Hu8cePGNG7cuESfLSLiDBW9vfDy9Cj2cosyYjo9PY2xY0cRFbWABx+sUSzPLxXfvPz9/Zk3bx6HDx8mMjKSzMxMfHx8eO2116hduzbHjh2ztYzq169vuy85OZnp06fz66+/YjAYePnll2ndujX79u1j9uzZAFSqVIm5c+eyZMkSAPr378/GjRsJCAjAz8+PixcvMnnyZJYtW8aaNWs4cOAA8+fPJzMzkytXrjBlyhS6dOlCWFgYFSpU4IcffiApKYmQkBD69evn+MoSESkCL08PhkxeW+zlFnbE9A8/HCUqaga//PK/Yn2+07955eTksHPnTvz8/AgPD2fu3Lls2bKFkSNHMm3aNABeeeUVJk2axJYtW6hVq5bt3jfffJN+/fqxefNmli5dyvTp00lLS2PJkiVERESwefNmWrduzbFjxwgPDwdg48aNAKSmpjJ69Gji4uJwd/8jh//73/9mxowZbNmyhRkzZvDWW2/Zzv36669ER0ezdOlSoqKiHFE9IiIubdu2Lbz00itUrVqtWMt1SsvrwoULBAYGApCdnU2TJk3o168f//nPfxg3bpzturS0NC5dusSFCxdo06YNAEFBQWzatAmAr7/+mlOnTrFw4UIATCYTv/zyC507d2b8+PF06dKFzp072+69WdOmTfMcmz17Nrt37+Y///kP//3vf0lP/2NuVps2bTAYDDz++ONcvny5SO+8cEqfPMeqVatYpDJKmik7i9TfXGd4rYiUfmFh00qk3FLxzQvg+PHj1KpVy3bcbDaTnJyMwWDAav1jQVuj0Wj72WKxsHr1aipXrgxcS4pVqlShQYMGdOzYkd27dzN79mwOHz6cKyle5+XllefYkCFD8Pf3x9/fn1atWjFp0iTbOU9PT+D2Pi4eWfYK2VdSinyfIz0xeQWg5CUipZ/Tuw2ve/TRR/ntt9/45ptvANi0aROTJk3Cx8eHGjVq8PnnnwPw4Ycf2u4JCAggOjoagJMnT9KrVy9+//13+vfvT3p6OiNGjGDEiBEcO3YMuJb4TCZTgTFcvnyZM2fOMHHiRJ566ik+/fRTzGZzCb2xiIjcrlIxYAOgXLlyvPXWW7z55ptkZWVRoUIFZs26tgfW7NmzmTJlCgsWLOBPf/qT7Z7w8HCmT59Or169AIiKiqJChQq89NJLhIWF4e7uzr333suMGTMA6Ny5M4GBgWzevDnfGCpXrsyzzz5Ljx49cHd3JyAggMzMTDIyMkr25UVEpEgM1hv75KTEuEq34cWLVx36zGrVKjr8ma5A9ZI/1UvBrtfNr7+e5YEHHs51vKRGGxblv8Wzz/Zi0aK3Cxwqf3Pcbm6GPAua36jUtLxERKT4ZWblEB01tETKLYqYmG3F+nwlLxGRMuzqlcwyuYNFqRmwISIiUlhqeTlI47GznB3CLZmys5wdgohIoSh5OUhKShoWi8bGiIgUB3UbioiIy1HyEhERl6PkJSIiLkffvBzE3mS7wijK3jkiItf5VCqHeznPYi+3KAt5v/vucj777BMAWrduwwsvTLzj5yt5OciEyFiSU9NvfWEBCrt3jojIjdzLeXIo6vliL7ewC3kfPBjPwYP7WbVq7f/tu/g39uzZTfv2He/o+UpeIiJSYqpUqUpIyIt4eFzbzfnhhx8hKenXOy5X37xERKTEPProY/j5NQbgl1/+x2effUKrVvnvsVgUSl4iIlLiTp36mRdfDCEkZCK1az90x+U5pdswMTGRbt268dhjjwHXNpVMT0+nT58+TJgwgSNHjrBu3TrefPPNAssICwujZcuWBAUF5Tp++PBhdu7cSWhoqO1YamoqI0aMACA5ORmAqlWrAvDee+/h4+NTnK8nIiI3OHz4e8LDX2HChJfo0qVrsZTptG9eN++mnJSURNeuXenRoweNGzemcePGt1XuyZMnSUnJvfWIj4+P7VmLFi0C4G9/+9ttRi4iIoWVlPQr//jHJF57LZInnniy2MotNQM2Ll68iNVqpXz58sTHx7N48WLWrFnDTz/9RFhYGGazmRYtWrB371527doFwOeff050dDQpKSmMHTuW7t27s3DhQjIyMli6dCnjxo275XNvfBb80aJr2bIlzz//PD4+Pnh5edGrVy+++OILfvvtN3755RfatGlDRERESVaJiMgdM2Vn/d/IwOIvtzA++ODfZGVls2jRfNuxPn2C6NPn2Tt6vtOS14ULFwgMDCQrK4vU1FQaN27M4sWLeeCBBzh79qzturCwMCZOnEj79u157733MJvNtnPZ2dls3LiRhIQEhg0bxsCBA5kwYQIHDhwoVOK6ldOnT7NixQpq1arF5s2b+e677/jwww8xGo1069aNwYMH4+vrW6iyFk7pk+t3iykHN3ePQsdS1L1zRESA/5uLVbj5WCXh73+fxN//PqnYy3V6t6HFYmHmzJn8/PPPtGmTewTK5cuXOXfuHO3btwegX79+vP/++7bznTt3xmAwUK9ePVJTU4s9xipVqlCrVi3b782aNaNChWuTjWvXrs1vv/1W6LJu3knZGbsWi4iUFU4fbejm5sbkyZNJSkpi5cqVuc4ZjUas1oJXYjcajQAYDIbbfr7BYMj1jJycP1o4Xl5eua719PQs8D4REXEcpycvAHd3dyZPnsySJUu4ePGi7XjFihWpXbs2e/bsAWDbtltvI200GjGZTIV+to+PD7/88gtZWVlcvnyZQ4cOFf0FRETEoUpF8gJ46qmnaNasGW+99Vau41FRUSxZsoS+ffty+PDhPK2hmzVp0oT//ve/zJkzp1DPrVevHu3bt6dHjx5MnDiRJ5544rbfQUSkNHC1XqHbiddgLeVvuXjxYgYMGED16tX5+OOP2bZtm224uyvRN6/8VatWUfWQD9VL/lQvBbteN8nJ5/Hyupfy5b3v6JOKo1itVtLTr5CZmUHVqg/ajru5GewuaF5qhsoXpEaNGowaNQp3d3e8vb3tTlwWEbnb+fhUIzX1Imlpl50dSqG5u5fDx6da0e4poViKTVBQUJ5VNEREJH9Go3uuFkxZVWq+eYmIiBRWqW95lRWNx87K9XthZ6eLiEheSl4OkpqajsWSe2yMm1vp/5jqCKqH/Kle8qd6KVhZqptbvUupH20oIiJyM33zEhERl6PkJSIiLkfJS0REXI6Sl4iIuBwlLxERcTlKXiIi4nKUvERExOUoeYmIiMtR8hIREZej5CUiIi5HyUtERFyOkpeIiLgcJS8REXE5Sl4iUiSjRo3i0qVLzg5D7nJKXiJSJF999ZWzQxBR8hIpS2JiYujRowe9evVi2LBhnD9/nvXr19OzZ0969+7NqFGjOH36NABhYWGsXLnSdu+Nv3fq1IlFixYxZMgQOnbsyIIFCwCYMmUKAMOHD+f8+fOOfTmRG2gnZZEy4vjx48yZM4ctW7bw4IMP8t577zFixAgsFgvr16/nvvvuY/PmzYSEhLB9+/ZblpeRkUF0dDRJSUn8+c9/pl+/fkRGRrJ582ZWr17Nfffd54C3EsmfWl4iZcS+ffto27YtDz74IAAjRoygc+fOPPPMM7ZEExQURFJSEomJibcsr3PnzgDcf//9VKlShd9++63kghcpIiUvkTLCaDRiMBhsv2dmZvLLL7/kuc5qtWIymTAYDFitVtvxnJycXNd5enrafr75WhFnU/ISKSP8/f3Zt28fFy5cAGDdunXs2bOHjz76yDY6cNOmTVSuXJmHH34YHx8fjh49CkBSUhIHDhwo1HOMRiMmk6lkXkKkkPTNS6SM8PX1JTQ0lOeffx6AatWqsWvXLj755BOGDx+OxWLhvvvu4+2338bNzY3g4GAmTZpE165dqVWrFgEBAYV6Trdu3QgODmbRokU8/vjjJflKIgUyWNUXICIiLkbdhiIi4nKUvERExOUoeYmIiMtR8hIREZej5CUiIi5HyUtERFyOkpeIiLgcJS8REXE5/x/2A+u5XNRCggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"COLLISIONTYPE\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Y and N needs to be mapped to 1 and 0, this will be part of data processing after train test split\n",
    "ax = sns.countplot(y=\"UNDERINFL\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"ADDRTYPE\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10', '11', '32', '23', '5', '22', '14', '30', ' ', '28', '51',\n",
       "       '13', '50', '12', '45', '0', '20', '21', '1', '52', '16', '15',\n",
       "       '74', '81', '26', '19', '2', '66', '71', '3', '24', '40', '57',\n",
       "       '6', '83', '25', '27', '4', '72', '29', '56', '73', '41', '17',\n",
       "       '65', '82', '67', '49', '84', '31', '43', '42', '48', '64', '53',\n",
       "       32, 50, 15, 10, 14, 20, 13, 22, 51, 11, 28, 12, 52, 21, 0, 19, 30,\n",
       "       16, 40, 26, 27, 83, 2, 45, 65, 23, 24, 71, 1, 29, 81, 25, 4, 73,\n",
       "       74, 72, 3, 84, 64, 57, 42, 41, 48, 66, 56, 31, 82, 67, '54', '60',\n",
       "       53, 43, 87, 54, '87', nan, '7', '8', '85', '88', '18'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the strings into int, this will be part of data processing after train test split\n",
    "data[\"ST_COLCODE\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(data[\"PERSONCOUNT\"])\n",
    "plt.ylabel(\"Fraction of data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"PEDCOUNT\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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dOt3vuSl6ALhKRFS09i+YHPBxe6WvkuRc9N9887W++eYrrV37jizL0p///F/avfszJSc/4NfcFD0ANBOtW7dRWtpMRUb+9g1XiYm3qKDgH36Pyxo9ADQTnTt30V13dZck5ef/Xbt25ahfvz/4PS5FDwDNzC+/HNXMmWlKS5uujh1v9ns8ih4AmpGDB7/XjBlTNWXKc/rjH0cEZEzW6AGgmSgo+Ifmzp2lv/xlvnr1+s+AjUvRA8BVqirK/3+HTODH9eXdd/9X5eUVWro0s/rY6NGPaPTosX7NTdEDwFV+2+vue7+7G2bMmKUZM2YFfFzW6AHAcBQ9ABiOogcAw1H0AK5hlmzbG+oQjWLbdqNfQ9EDuGZFRbXS+fNnVVVV2aQCDTbbtlVaelG/+12rRr2OXTcArllxcR6VlFxQYWGBvN4roY7TIBERUeratZPOn2/4F5hT9ACuWZZlKSYmVjExsaGO0ii/3fSs4UXP0g0AGI6iBwDDNdulm7AwK9QRmozsoUH20CB7aPx7dqf3Ytkt4aNmAECTsXQDAIaj6AHAcBQ9ABiOogcAw1H0AGA4ih4ADEfRA4DhKHoAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABgOIoecFFqaqoKCwtDHQPXOIoecNGePXtCHQGg6HHt2rJli4YPH66UlBRNmDBBp06d0qZNmzRixAiNHDlSqampOnbsmCRp9uzZWr16dfVrr3784IMPaunSpXriiSf0wAMPaPHixZKkOXPmSJImTpyoU6dOBffNAVdptt8wBbgpLy9Pr7/+urKystS+fXutW7dOkyZNktfr1aZNmxQfH69t27YpLS1NH330kc/xLl26pI0bN6qgoECDBw/WmDFjNH/+fG3btk3r169XfHx8EN4VUDeu6HFNys3N1YABA9S+fXtJ0qRJkzRo0CANGzasupQfeeQRFRQU6MSJEz7HGzRokCQpISFBrVu31oULF9wLDzQSRY9rUnh4uCzrX9+xWVZWpvz8/Frn2batqqoqWZalq791s7KyssZ50dHR1b//+7lAqFH0uCb16dNHubm5On36tCTpvffe0+7du/Xxxx9X75LZunWrYmNjlZiYqLi4OP3444+SpIKCAu3bt69B84SHh6uqqsqdNwE0EGv0uCbddtttev755zV58mRJksfjUXZ2tnJycjRx4kR5vV7Fx8drxYoVCgsL0/jx4zVr1iwNHTpUN910k/r27dugeR566CGNHz9eS5cuVbdu3dx8S0C9LJv/YwKA0Vi6AQDDUfQAYDiKHgAMR9EDgOEoegAwHEUPAIaj6AHAcBQ9ABju/wBI060xob2R3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"PEDCYLCOUNT\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"VEHCOUNT\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched      189786\n",
      "Unmatched      4887\n",
      "Name: STATUS, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(data[\"STATUS\"].value_counts())\n",
    "df = data.groupby(by=[\"STATUS\", \"SEVERITYCODE\"]).count()[\"OBJECTID\"]\n",
    "df.unstack().transpose().plot.pie(subplots=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aryam\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"HITPARKEDCAR\", hue=\"SEVERITYCODE\", data=data)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>SEVERITYCODE</th>\n",
       "      <th>ADDRTYPE</th>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <th>UNDERINFL</th>\n",
       "      <th>WEATHER</th>\n",
       "      <th>ROADCOND</th>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Angles</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>At Intersection (intersection related)</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Block</td>\n",
       "      <td>Sideswipe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mid-Block (not related to intersection)</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Raining</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Block</td>\n",
       "      <td>Parked Car</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mid-Block (not related to intersection)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Block</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mid-Block (not related to intersection)</td>\n",
       "      <td>11</td>\n",
       "      <td>N</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Angles</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>At Intersection (intersection related)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Raining</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEVERITYCODE      ADDRTYPE COLLISIONTYPE  PERSONCOUNT  PEDCOUNT  \\\n",
       "0             2  Intersection        Angles            2         0   \n",
       "1             1         Block     Sideswipe            2         0   \n",
       "2             1         Block    Parked Car            4         0   \n",
       "3             1         Block         Other            3         0   \n",
       "4             2  Intersection        Angles            2         0   \n",
       "\n",
       "   PEDCYLCOUNT  VEHCOUNT                             JUNCTIONTYPE  \\\n",
       "0            0         2   At Intersection (intersection related)   \n",
       "1            0         2  Mid-Block (not related to intersection)   \n",
       "2            0         3  Mid-Block (not related to intersection)   \n",
       "3            0         3  Mid-Block (not related to intersection)   \n",
       "4            0         2   At Intersection (intersection related)   \n",
       "\n",
       "   SDOT_COLCODE UNDERINFL   WEATHER ROADCOND                LIGHTCOND  \\\n",
       "0            11         N  Overcast      Wet                 Daylight   \n",
       "1            16         0   Raining      Wet  Dark - Street Lights On   \n",
       "2            14         0  Overcast      Dry                 Daylight   \n",
       "3            11         N     Clear      Dry                 Daylight   \n",
       "4            11         0   Raining      Wet                 Daylight   \n",
       "\n",
       "  ST_COLCODE  SEGLANEKEY  CROSSWALKKEY HITPARKEDCAR  \n",
       "0         10           0             0            N  \n",
       "1         11           0             0            N  \n",
       "2         32           0             0            N  \n",
       "3         23           0             0            N  \n",
       "4         10           0             0            N  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets drop the columns which are not usefull for our model.\n",
    "drop_cols = [\"SEVERITYCODE.1\", \"SEVERITYDESC\", \"SDOTCOLNUM\", \"X\", \"Y\", \"ST_COLDESC\", \"LOCATION\", \"SDOT_COLDESC\", \n",
    "             \"INCDTTM\", \"INCDATE\", \"REPORTNO\", \"COLDETKEY\", \"INCKEY\", \"OBJECTID\", \"STATUS\"]\n",
    "data.drop(columns=drop_cols, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Data Types</th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>6329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER</th>\n",
       "      <td>object</td>\n",
       "      <td>5081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROADCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>5012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>4904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNDERINFL</th>\n",
       "      <td>object</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADDRTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <td>object</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Data Types  Null Values\n",
       "JUNCTIONTYPE      object         6329\n",
       "LIGHTCOND         object         5170\n",
       "WEATHER           object         5081\n",
       "ROADCOND          object         5012\n",
       "COLLISIONTYPE     object         4904\n",
       "UNDERINFL         object         4884\n",
       "ADDRTYPE          object         1926\n",
       "ST_COLCODE        object           18\n",
       "SEVERITYCODE       int64            0\n",
       "CROSSWALKKEY       int64            0\n",
       "SEGLANEKEY         int64            0\n",
       "SDOT_COLCODE       int64            0\n",
       "VEHCOUNT           int64            0\n",
       "PEDCYLCOUNT        int64            0\n",
       "PEDCOUNT           int64            0\n",
       "PERSONCOUNT        int64            0\n",
       "HITPARKEDCAR      object            0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([data.dtypes, data.isna().sum()], axis=1, \n",
    "          keys=[\"Data Types\", \"Null Values\"]).sort_values(by=[\"Null Values\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y = data[[\"SEVERITYCODE\"]]\n",
    "data_X = data.drop(columns=[\"SEVERITYCODE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, test_size=0.30, random_state=42, stratify=data_y, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    126438\n",
       "Y      6373\n",
       "Name: UNDERINFL, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None\n",
    "X_train[\"UNDERINFL\"] = X_train[\"UNDERINFL\"].apply(lambda x: 'N' if x=='0' else 'Y' if x=='1' else x)\n",
    "X_train[\"UNDERINFL\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14., 28., 13., 32., nan,  0., 50., 22., 10., 26., 20., 15., 11.,\n",
       "       23., 71., 45., 52., 81., 51., 12., 24., 16., 21.,  1., 29., 88.,\n",
       "        3.,  2.,  6., 74., 30., 27., 57., 83., 19., 56.,  5., 53., 31.,\n",
       "       73., 84.,  8.,  4., 40., 25., 72., 17., 41., 82., 54., 65., 48.,\n",
       "       64.,  7., 66., 85., 67., 42., 18., 43., 60., 49., 87.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "X_train[\"ST_COLCODE\"] = X_train[\"ST_COLCODE\"].apply(lambda x: np.nan if x==' ' else int(x) if isinstance(x, str) else x)\n",
    "X_train[\"ST_COLCODE\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total distribution of training data:\n",
      "1    95539\n",
      "2    40732\n",
      "Name: SEVERITYCODE, dtype: int64\n",
      "\n",
      "Total distribution of missing data we are planning to drop:\n",
      "1    7151\n",
      "2    1086\n",
      "Name: SEVERITYCODE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train = pd.concat([X_train, y_train], axis=1)\n",
    "print(\"Total distribution of training data:\")\n",
    "print(train['SEVERITYCODE'].value_counts())\n",
    "print(\"\")\n",
    "print(\"Total distribution of missing data we are planning to drop:\")\n",
    "print(train[train.isna().any(axis=1)]['SEVERITYCODE'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Data Types</th>\n",
       "      <th>Null Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADDRTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROADCOND</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNDERINFL</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVERITYCODE</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Data Types  Null Values\n",
       "ADDRTYPE          object            0\n",
       "WEATHER           object            0\n",
       "HITPARKEDCAR      object            0\n",
       "CROSSWALKKEY       int64            0\n",
       "SEGLANEKEY         int64            0\n",
       "ST_COLCODE       float64            0\n",
       "LIGHTCOND         object            0\n",
       "ROADCOND          object            0\n",
       "UNDERINFL         object            0\n",
       "COLLISIONTYPE     object            0\n",
       "SDOT_COLCODE       int64            0\n",
       "JUNCTIONTYPE      object            0\n",
       "VEHCOUNT           int64            0\n",
       "PEDCYLCOUNT        int64            0\n",
       "PEDCOUNT           int64            0\n",
       "PERSONCOUNT        int64            0\n",
       "SEVERITYCODE       int64            0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It seems safe to drop the missing data rows as the proportion of data dropped is okay\n",
    "train = train.dropna()\n",
    "y_train = train[[\"SEVERITYCODE\"]]\n",
    "X_train = train.drop(columns=[\"SEVERITYCODE\"])\n",
    "pd.concat([train.dtypes, train.isna().sum()], axis=1, \n",
    "          keys=[\"Data Types\", \"Null Values\"]).sort_values(by=[\"Null Values\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparing X and Y for modelling\n",
    "y_train = y_train.values.ravel()\n",
    "X_train.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature SEGLANEKEY, training categories: 1451\n",
      "Feature CROSSWALKKEY, training categories: 1733\n",
      "Feature SDOT_COLCODE, training categories: 39\n",
      "Feature ST_COLCODE, training categories: 62\n"
     ]
    }
   ],
   "source": [
    "# Features with many categories, pending to experiment with\n",
    "print(\"Feature SEGLANEKEY, training categories:\",len(X_train[\"SEGLANEKEY\"].unique()))\n",
    "print(\"Feature CROSSWALKKEY, training categories:\",len(X_train[\"CROSSWALKKEY\"].unique()))\n",
    "print(\"Feature SDOT_COLCODE, training categories:\",len(X_train[\"SDOT_COLCODE\"].unique()))\n",
    "print(\"Feature ST_COLCODE, training categories:\",len(X_train[\"ST_COLCODE\"].unique()))\n",
    "#X_train.drop(columns=[\"SEGLANEKEY\", \"CROSSWALKKEY\", \"SDOT_COLCODE\", \"ST_COLCODE\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"HITPARKEDCAR\"] = X_train[\"HITPARKEDCAR\"].apply(lambda x: 1 if x=='Y' else 0)\n",
    "X_train[\"UNDERINFL\"] = X_train[\"UNDERINFL\"].apply(lambda x: 1 if x=='Y' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encoding\n",
    "oh_encodings = {}\n",
    "def one_hot(df, cols):\n",
    "    global oh_encodings\n",
    "    for col in cols:\n",
    "        dummy = pd.get_dummies(df[col], prefix=col)\n",
    "        if col+\"_Unknown\" in dummy.columns:\n",
    "            dummy.drop(columns=[col+\"_Unknown\"], inplace=True)\n",
    "        elif col+\"_Others\" in dummy.columns:\n",
    "            dummy.drop(columns=[col+\"_Other\"], inplace=True)\n",
    "        else:\n",
    "            dummy.drop(columns=[dummy.columns[-1]], inplace=True)\n",
    "        oh_encodings[col] = list(dummy.columns)\n",
    "        df = pd.concat([df, dummy], axis=1)\n",
    "        df.drop(columns=[col], inplace = True)  \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Hashing for very high cardinality nominal features\n",
    "hashing_dict = {}\n",
    "from copy import deepcopy\n",
    "from sklearn.feature_extraction import FeatureHasher\n",
    "def featurehash(df, cols):\n",
    "    global hashing_dict\n",
    "    fh = FeatureHasher(n_features=30, input_type='string')\n",
    "    for col in cols:\n",
    "        df[col] = df[col].astype(str)\n",
    "        hashed_features = fh.fit_transform(df[col])\n",
    "        fh_fit = deepcopy(fh)\n",
    "        hashing_dict[col] = fh_fit\n",
    "        hashed_features = hashed_features.toarray()\n",
    "        dummy = pd.DataFrame(hashed_features)\n",
    "        for name in dummy.columns:\n",
    "            dummy.rename(columns={name:col+'_'+str(name)}, inplace=True)\n",
    "        df = pd.concat([df, dummy], axis=1)\n",
    "        df.drop(columns=[col], inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ADDRTYPE</th>\n",
       "      <th>COLLISIONTYPE</th>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <th>JUNCTIONTYPE</th>\n",
       "      <th>SDOT_COLCODE</th>\n",
       "      <th>UNDERINFL</th>\n",
       "      <th>WEATHER</th>\n",
       "      <th>ROADCOND</th>\n",
       "      <th>LIGHTCOND</th>\n",
       "      <th>ST_COLCODE</th>\n",
       "      <th>SEGLANEKEY</th>\n",
       "      <th>CROSSWALKKEY</th>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Block</td>\n",
       "      <td>Rear Ended</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mid-Block (but intersection related)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Intersection</td>\n",
       "      <td>Left Turn</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>At Intersection (intersection related)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Block</td>\n",
       "      <td>Rear Ended</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mid-Block (but intersection related)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Block</td>\n",
       "      <td>Rear Ended</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mid-Block (not related to intersection)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Block</td>\n",
       "      <td>Parked Car</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mid-Block (not related to intersection)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ADDRTYPE COLLISIONTYPE  PERSONCOUNT  PEDCOUNT  PEDCYLCOUNT  VEHCOUNT  \\\n",
       "0         Block    Rear Ended            2         0            0         2   \n",
       "1  Intersection     Left Turn            2         0            0         2   \n",
       "2         Block    Rear Ended            4         0            0         2   \n",
       "3         Block    Rear Ended            3         0            0         2   \n",
       "4         Block    Parked Car            2         0            0         2   \n",
       "\n",
       "                              JUNCTIONTYPE  SDOT_COLCODE  UNDERINFL WEATHER  \\\n",
       "0     Mid-Block (but intersection related)            14          0   Clear   \n",
       "1   At Intersection (intersection related)            11          0   Clear   \n",
       "2     Mid-Block (but intersection related)            14          0   Clear   \n",
       "3  Mid-Block (not related to intersection)            14          0   Clear   \n",
       "4  Mid-Block (not related to intersection)            11          0   Clear   \n",
       "\n",
       "  ROADCOND                LIGHTCOND  ST_COLCODE  SEGLANEKEY  CROSSWALKKEY  \\\n",
       "0      Wet                 Daylight        14.0           0             0   \n",
       "1      Dry                 Daylight        28.0           0             0   \n",
       "2      Dry                 Daylight        14.0           0             0   \n",
       "3      Dry  Dark - Street Lights On        13.0           0             0   \n",
       "4      Dry                 Daylight        32.0           0             0   \n",
       "\n",
       "   HITPARKEDCAR  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERSONCOUNT</th>\n",
       "      <th>PEDCOUNT</th>\n",
       "      <th>PEDCYLCOUNT</th>\n",
       "      <th>VEHCOUNT</th>\n",
       "      <th>UNDERINFL</th>\n",
       "      <th>HITPARKEDCAR</th>\n",
       "      <th>ADDRTYPE_Alley</th>\n",
       "      <th>ADDRTYPE_Block</th>\n",
       "      <th>WEATHER_Blowing Sand/Dirt</th>\n",
       "      <th>WEATHER_Clear</th>\n",
       "      <th>...</th>\n",
       "      <th>CROSSWALKKEY_20</th>\n",
       "      <th>CROSSWALKKEY_21</th>\n",
       "      <th>CROSSWALKKEY_22</th>\n",
       "      <th>CROSSWALKKEY_23</th>\n",
       "      <th>CROSSWALKKEY_24</th>\n",
       "      <th>CROSSWALKKEY_25</th>\n",
       "      <th>CROSSWALKKEY_26</th>\n",
       "      <th>CROSSWALKKEY_27</th>\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>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",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 208 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PERSONCOUNT  PEDCOUNT  PEDCYLCOUNT  VEHCOUNT  UNDERINFL  HITPARKEDCAR  \\\n",
       "0            2         0            0         2          0             0   \n",
       "1            2         0            0         2          0             0   \n",
       "2            4         0            0         2          0             0   \n",
       "3            3         0            0         2          0             0   \n",
       "4            2         0            0         2          0             0   \n",
       "\n",
       "   ADDRTYPE_Alley  ADDRTYPE_Block  WEATHER_Blowing Sand/Dirt  WEATHER_Clear  \\\n",
       "0               0               1                          0              1   \n",
       "1               0               0                          0              1   \n",
       "2               0               1                          0              1   \n",
       "3               0               1                          0              1   \n",
       "4               0               1                          0              1   \n",
       "\n",
       "   ...  CROSSWALKKEY_20  CROSSWALKKEY_21  CROSSWALKKEY_22  CROSSWALKKEY_23  \\\n",
       "0  ...              0.0              0.0              0.0              0.0   \n",
       "1  ...              0.0              0.0              0.0              0.0   \n",
       "2  ...              0.0              0.0              0.0              0.0   \n",
       "3  ...              0.0              0.0              0.0              0.0   \n",
       "4  ...              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   CROSSWALKKEY_24  CROSSWALKKEY_25  CROSSWALKKEY_26  CROSSWALKKEY_27  \\\n",
       "0              0.0              0.0              0.0              0.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   CROSSWALKKEY_28  CROSSWALKKEY_29  \n",
       "0              0.0              0.0  \n",
       "1              0.0              0.0  \n",
       "2              0.0              0.0  \n",
       "3              0.0              0.0  \n",
       "4              0.0              0.0  \n",
       "\n",
       "[5 rows x 208 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oh_cols = [\"ADDRTYPE\", \"WEATHER\", \"LIGHTCOND\", \"ROADCOND\", \"COLLISIONTYPE\", \"JUNCTIONTYPE\",\"SDOT_COLCODE\", \"ST_COLCODE\"]\n",
    "X_train = one_hot(X_train, oh_cols)\n",
    "hash_cols = [\"SEGLANEKEY\", \"CROSSWALKKEY\"]\n",
    "X_train = featurehash(X_train, hash_cols)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>173</th>\n",
       "      <td>SEGLANEKEY_25</td>\n",
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       "      <th>172</th>\n",
       "      <td>SEGLANEKEY_24</td>\n",
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       "      <th>164</th>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>SEGLANEKEY_27</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>SEGLANEKEY_15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>SEGLANEKEY_12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>SEGLANEKEY_10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>SEGLANEKEY_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>SEGLANEKEY_6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>SEGLANEKEY_5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>SEGLANEKEY_4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>SEGLANEKEY_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>CROSSWALKKEY_28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>SEGLANEKEY_13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>SEGLANEKEY_28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>SEGLANEKEY_14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>SEGLANEKEY_29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>CROSSWALKKEY_26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>CROSSWALKKEY_25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>CROSSWALKKEY_24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>CROSSWALKKEY_23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>CROSSWALKKEY_22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>CROSSWALKKEY_18</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>CROSSWALKKEY_17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>CROSSWALKKEY_16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>CROSSWALKKEY_27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>CROSSWALKKEY_15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>CROSSWALKKEY_13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>CROSSWALKKEY_12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>CROSSWALKKEY_10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>CROSSWALKKEY_9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>CROSSWALKKEY_6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>CROSSWALKKEY_5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>CROSSWALKKEY_4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature  Importance\n",
       "207  CROSSWALKKEY_29         0.0\n",
       "174    SEGLANEKEY_26         0.0\n",
       "173    SEGLANEKEY_25         0.0\n",
       "172    SEGLANEKEY_24         0.0\n",
       "171    SEGLANEKEY_23         0.0\n",
       "170    SEGLANEKEY_22         0.0\n",
       "166    SEGLANEKEY_18         0.0\n",
       "165    SEGLANEKEY_17         0.0\n",
       "164    SEGLANEKEY_16         0.0\n",
       "175    SEGLANEKEY_27         0.0\n",
       "163    SEGLANEKEY_15         0.0\n",
       "160    SEGLANEKEY_12         0.0\n",
       "158    SEGLANEKEY_10         0.0\n",
       "157     SEGLANEKEY_9         0.0\n",
       "154     SEGLANEKEY_6         0.0\n",
       "153     SEGLANEKEY_5         0.0\n",
       "152     SEGLANEKEY_4         0.0\n",
       "149     SEGLANEKEY_1         0.0\n",
       "206  CROSSWALKKEY_28         0.0\n",
       "161    SEGLANEKEY_13         0.0\n",
       "176    SEGLANEKEY_28         0.0\n",
       "162    SEGLANEKEY_14         0.0\n",
       "177    SEGLANEKEY_29         0.0\n",
       "204  CROSSWALKKEY_26         0.0\n",
       "203  CROSSWALKKEY_25         0.0\n",
       "202  CROSSWALKKEY_24         0.0\n",
       "201  CROSSWALKKEY_23         0.0\n",
       "200  CROSSWALKKEY_22         0.0\n",
       "196  CROSSWALKKEY_18         0.0\n",
       "195  CROSSWALKKEY_17         0.0\n",
       "194  CROSSWALKKEY_16         0.0\n",
       "205  CROSSWALKKEY_27         0.0\n",
       "193  CROSSWALKKEY_15         0.0\n",
       "191  CROSSWALKKEY_13         0.0\n",
       "190  CROSSWALKKEY_12         0.0\n",
       "188  CROSSWALKKEY_10         0.0\n",
       "187   CROSSWALKKEY_9         0.0\n",
       "184   CROSSWALKKEY_6         0.0\n",
       "183   CROSSWALKKEY_5         0.0\n",
       "182   CROSSWALKKEY_4         0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "importance = model.feature_importances_\n",
    "# summarize feature importance\n",
    "less_imp = pd.DataFrame(columns=[\"Feature\", \"Importance\"]) \n",
    "for i,v in enumerate(importance):\n",
    "    less_imp = less_imp.append({\"Feature\":X_train.columns[i], \"Importance\":v}, ignore_index=True)\n",
    "less_imp.sort_values(by=[\"Importance\"], inplace=True)\n",
    "less_imp.iloc[:40, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping 10 least important features\n",
    "X_train.drop(columns=less_imp.iloc[:40, :][\"Feature\"].to_list(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = X_train.corr()\n",
    "sns.heatmap(corr, vmin=-1, vmax=1, center= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PEDCOUNT COLLISIONTYPE_Pedestrian 0.9455974863712838\n",
      "PEDCOUNT SDOT_COLCODE_24 0.9284001509821092\n",
      "PEDCYLCOUNT COLLISIONTYPE_Cycles 0.989863709514695\n",
      "PEDCYLCOUNT ST_COLCODE_45.0 0.9213836138998203\n",
      "ADDRTYPE_Block JUNCTIONTYPE_At Intersection (intersection related) -0.9714923909985959\n",
      "COLLISIONTYPE_Angles ST_COLCODE_10.0 1.0\n",
      "COLLISIONTYPE_Cycles ST_COLCODE_45.0 0.9305288610413508\n",
      "COLLISIONTYPE_Parked Car ST_COLCODE_32.0 0.9472288774207903\n",
      "COLLISIONTYPE_Pedestrian SDOT_COLCODE_24 0.9747115164191715\n",
      "COLLISIONTYPE_Right Turn ST_COLCODE_16.0 1.0\n"
     ]
    }
   ],
   "source": [
    "# Finding features with > 90% correlation\n",
    "import itertools\n",
    "for i, j in itertools.combinations(corr.columns, 2):\n",
    "    if abs(corr.loc[i, j])>0.90:\n",
    "        print(i,j, corr.loc[i, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping these highly correlated columns\n",
    "X_train.drop(columns=[\"COLLISIONTYPE_Pedestrian\", \"COLLISIONTYPE_Cycles\", \"JUNCTIONTYPE_At Intersection (intersection related)\", \n",
    "                      \"SDOT_COLCODE_24\", \"ST_COLCODE_45.0\", \"ST_COLCODE_10.0\", \"ST_COLCODE_32.0\", \"ST_COLCODE_16.0\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report,roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Random Forest\n",
      "Accuracy: 74.22%\n",
      "Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.77      0.90      0.83     88388\n",
      "           2       0.64      0.39      0.48     39646\n",
      "\n",
      "    accuracy                           0.74    128034\n",
      "   macro avg       0.70      0.64      0.66    128034\n",
      "weighted avg       0.73      0.74      0.72    128034\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rnf = RandomForestClassifier(random_state = 42)\n",
    "train_predicted = cross_val_predict(rnf, X_train, y_train, cv=5)\n",
    "accuracy = accuracy_score(y_train, train_predicted)\n",
    "print(\"Model: Random Forest\")\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Report:\")\n",
    "print(classification_report(y_train,train_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: XG Boost\n",
      "Accuracy: 75.49%\n",
      "Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.94      0.84     88388\n",
      "           2       0.71      0.35      0.47     39646\n",
      "\n",
      "    accuracy                           0.75    128034\n",
      "   macro avg       0.74      0.64      0.66    128034\n",
      "weighted avg       0.75      0.75      0.73    128034\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = XGBClassifier(random_state = 42)\n",
    "train_predicted = cross_val_predict(xgb_clf, X_train, y_train, cv=5)\n",
    "accuracy = accuracy_score(y_train, train_predicted)\n",
    "print(\"Model: XG Boost\")\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Report:\")\n",
    "print(classification_report(y_train,train_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 69.42%\n",
      "Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.86      0.66      0.75     88388\n",
      "           2       0.50      0.76      0.61     39646\n",
      "\n",
      "    accuracy                           0.69    128034\n",
      "   macro avg       0.68      0.71      0.68    128034\n",
      "weighted avg       0.75      0.69      0.71    128034\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Not Using Oversampling for now as it is decreasing accuracy and not improving f1 very much.\n",
    "\n",
    "# Scoring with oversampling to remove imballance\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "kf = StratifiedKFold(n_splits=10)\n",
    "cv_actual = []\n",
    "cv_pred = []\n",
    "X_train_numpy = X_train.to_numpy()\n",
    "\n",
    "for fold, (train_index, test_index) in enumerate(kf.split(X_train_numpy, y_train), 1):\n",
    "    X_train_cv = X_train_numpy[train_index]\n",
    "    y_train_cv = y_train[train_index]\n",
    "    X_test_cv = X_train_numpy[test_index]\n",
    "    y_test_cv = y_train[test_index]\n",
    "    sm = SMOTE()\n",
    "    X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train_cv, y_train_cv)\n",
    "    model = xgb_clf\n",
    "    model.fit(X_train_oversampled, y_train_oversampled )  \n",
    "    y_pred = model.predict(X_test_cv)\n",
    "    cv_pred.extend(y_pred)\n",
    "    cv_actual.extend(y_test_cv)\n",
    "\n",
    "accuracy = accuracy_score(cv_actual, cv_pred)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Report:\")\n",
    "print(classification_report(cv_actual,cv_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 out of  45 | elapsed: 18.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 7, 'min_child_weight': 4} 0.8000032906589803\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# max_depth - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.\n",
    "# min_child_weight - Minimum sum of instance weight (hessian) needed in a child.\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=140, max_depth=6, \n",
    "                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, \n",
    "                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1)\n",
    "\n",
    "param_test1 = {'max_depth':range(5,8,1), 'min_child_weight':range(3,6,1)}\n",
    "\n",
    "grid_search1 = GridSearchCV(estimator=xgb_clf, param_grid=param_test1, scoring='roc_auc', n_jobs=4, cv=5, verbose=True)\n",
    "\n",
    "grid_search1.fit(X_train,y_train)\n",
    "print(grid_search1.best_params_, grid_search1.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  15 out of  15 | elapsed:  6.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.0} 0.799968091097784\n"
     ]
    }
   ],
   "source": [
    "# gamma/min_split_loss - Minimum loss reduction required to make a further partition on a leaf node of the tree. \n",
    "# The larger gamma is, the more conservative the algorithm will be.\n",
    "\n",
    "param_test2 = {'gamma':[i/10.0 for i in range(0,3)]}\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=140, max_depth=6, \n",
    "                        min_child_weight=4, gamma=0, subsample=0.8, colsample_bytree=0.8, \n",
    "                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1)\n",
    "\n",
    "grid_search2 = GridSearchCV(estimator=xgb_clf, param_grid=param_test2, scoring='roc_auc', n_jobs=4, cv=5, verbose=True)\n",
    "\n",
    "grid_search2.fit(X_train,y_train)\n",
    "print(grid_search2.best_params_, grid_search2.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 out of  45 | elapsed: 17.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bytree': 0.8, 'subsample': 0.8} 0.799968091097784\n"
     ]
    }
   ],
   "source": [
    "# subsample - Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing trees. \n",
    "# and this will prevent overfitting.Subsampling will occur once in every boosting iteration.\n",
    "\n",
    "# colsample_bytree - Parameter for subsampling of columns. The subsample ratio of columns when constructing each tree. \n",
    "# Subsampling occurs once for every tree constructed.\n",
    "\n",
    "param_test3 = {'subsample':[i/10.0 for i in range(6,9)], 'colsample_bytree':[i/10.0 for i in range(6,9)]}\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=140, max_depth=6, \n",
    "                        min_child_weight=4, gamma=0, subsample=0.8, colsample_bytree=0.8, \n",
    "                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1)\n",
    "\n",
    "grid_search3 = GridSearchCV(estimator=xgb_clf, param_grid=param_test3, scoring='roc_auc', n_jobs=4, cv=5, verbose=True)\n",
    "\n",
    "grid_search3.fit(X_train,y_train)\n",
    "print(grid_search3.best_params_, grid_search3.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 out of  45 | elapsed: 17.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'reg_alpha': 1, 'reg_lambda': 0.05} 0.8000505285639103\n"
     ]
    }
   ],
   "source": [
    "# alpha/reg_alpha - L1 regularization term on weights. Increasing this value will make model more conservative.\n",
    "# lambda/reg_lambda - L2 regularization term on weights. Increasing this value will make model more conservative.\n",
    "\n",
    "param_test4 = {'reg_alpha':[0.5, 1, 10], 'reg_lambda':[0.00005, 0.05, 0.1]}\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=140, max_depth=6, reg_alpha=1,\n",
    "                        min_child_weight=4, gamma=0, subsample=0.7, colsample_bytree=0.7, \n",
    "                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, reg_lambda=0.05)\n",
    "\n",
    "grid_search4 = GridSearchCV(estimator=xgb_clf, param_grid=param_test4, scoring='roc_auc', n_jobs=4, cv=5, verbose=True)\n",
    "\n",
    "grid_search4.fit(X_train,y_train)\n",
    "print(grid_search4.best_params_, grid_search4.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 out of  45 | elapsed: 23.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'n_estimators': 170} 0.8001438064465919\n"
     ]
    }
   ],
   "source": [
    "# eta/learning_rate - Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, \n",
    "# and eta shrinks the feature weights to make the boosting process more conservative.\n",
    "\n",
    "param_test5 = {'learning_rate':[0.05, 0.1, 0.3], 'n_estimators':[160, 170, 180]}\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=170, max_depth=6, reg_alpha=1,\n",
    "                        min_child_weight=4, gamma=0, subsample=0.7, colsample_bytree=0.7, \n",
    "                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, reg_lambda=0.05)\n",
    "\n",
    "grid_search5 = GridSearchCV(estimator=xgb_clf, param_grid=param_test5, scoring='roc_auc', n_jobs=4, cv=5, verbose=True)\n",
    "\n",
    "grid_search5.fit(X_train,y_train)\n",
    "print(grid_search5.best_params_, grid_search5.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: XG Boost\n",
      "Accuracy: 75.50%\n",
      "Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.94      0.84     88388\n",
      "           2       0.71      0.35      0.47     39646\n",
      "\n",
      "    accuracy                           0.76    128034\n",
      "   macro avg       0.74      0.64      0.65    128034\n",
      "weighted avg       0.75      0.76      0.73    128034\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=170, max_depth=6, reg_alpha=1, \n",
    "                        min_child_weight=4, gamma=0, subsample=0.7, colsample_bytree=0.7, objective= 'binary:logistic', \n",
    "                        nthread=4, scale_pos_weight=1, reg_lambda=0.05)\n",
    "train_predicted = cross_val_predict(xgb_clf, X_train, y_train, cv=5)\n",
    "accuracy = accuracy_score(y_train, train_predicted)\n",
    "print(\"Model: XG Boost\")\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Report:\")\n",
    "print(classification_report(y_train,train_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.7, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=4, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=170, n_jobs=4, nthread=4, num_parallel_tree=1,\n",
       "              random_state=42, reg_alpha=1, reg_lambda=0.05, scale_pos_weight=1,\n",
       "              subsample=0.7, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = XGBClassifier(random_state = 42, learning_rate =0.1, n_estimators=170, max_depth=6, reg_alpha=1, \n",
    "                            min_child_weight=4, gamma=0, subsample=0.7, colsample_bytree=0.7, \n",
    "                            objective= 'binary:logistic', nthread=4, scale_pos_weight=1, reg_lambda=0.05)\n",
    "final_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\aryam\\\\anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Learning curve wrt xgboost model\n",
    "\n",
    "from sklearn.model_selection import learning_curve\n",
    "def Learning_curve_model(X, Y, model, cv, train_sizes):\n",
    "\n",
    "    plt.figure()\n",
    "    plt.title(\"Learning curve\")\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "\n",
    "    train_sizes, train_scores, test_scores = learning_curve(model, X, Y, cv=cv, n_jobs=4, train_sizes=train_sizes)\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std  = np.std(train_scores, axis=1)\n",
    "    test_scores_mean  = np.mean(test_scores, axis=1)\n",
    "    test_scores_std   = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",label=\"Cross-validation score\")\n",
    "                     \n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "cv = 10\n",
    "train_size=np.linspace(.1, 1.0, 15)\n",
    "Learning_curve_model(X_train, y_train, final_model, cv, train_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_test(df):\n",
    "    global oh_encodings\n",
    "    for col, encodes in oh_encodings.items():\n",
    "        for encode in encodes:\n",
    "            category = encode.split('_')[1]\n",
    "            df[encode] = df[col].apply(lambda x: 1 if x==category else 0)\n",
    "        df.drop(columns=[col], inplace = True)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featurehashing_test(df):\n",
    "    global hashing_dict\n",
    "    for col, fh in hashing_dict.items():\n",
    "        df[col] = df[col].astype(str)\n",
    "        hashed_features = fh.transform(df[col])\n",
    "        hashed_features = hashed_features.toarray()\n",
    "        dummy = pd.DataFrame(hashed_features)\n",
    "        for name in dummy.columns:\n",
    "            dummy.rename(columns={name:col+'_'+str(name)}, inplace=True)\n",
    "        df = pd.concat([df, dummy], axis=1)\n",
    "        df.drop(columns=[col], inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(df):\n",
    "    global less_imp\n",
    "    df[\"UNDERINFL\"] = df[\"UNDERINFL\"].apply(lambda x: 'N' if x=='0' else 'Y' if x=='1' else x)\n",
    "    df[\"ST_COLCODE\"] = df[\"ST_COLCODE\"].apply(lambda x: np.nan if x==' ' else int(x) if isinstance(x, str) else x)\n",
    "    df[\"HITPARKEDCAR\"] = df[\"HITPARKEDCAR\"].apply(lambda x: 1 if x=='Y' else 0)\n",
    "    df[\"UNDERINFL\"] = df[\"UNDERINFL\"].apply(lambda x: 1 if x=='Y' else 0)\n",
    "    df = one_hot_test(df)\n",
    "    df = featurehashing_test(df)\n",
    "    df.drop(columns=less_imp.iloc[:40, :][\"Feature\"].to_list(), inplace=True)\n",
    "    df.drop(columns=[\"COLLISIONTYPE_Pedestrian\", \"COLLISIONTYPE_Cycles\", \n",
    "                     \"JUNCTIONTYPE_At Intersection (intersection related)\", \"SDOT_COLCODE_24\", \n",
    "                     \"ST_COLCODE_45.0\", \"ST_COLCODE_10.0\", \"ST_COLCODE_32.0\", \"ST_COLCODE_16.0\"], inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.values.ravel()\n",
    "X_test.reset_index(drop=True, inplace=True)\n",
    "X_test = preprocessing(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = final_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Result\n",
      "Accuracy: 75.82%\n",
      "Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.75      0.97      0.85     40946\n",
      "           2       0.81      0.25      0.38     17456\n",
      "\n",
      "    accuracy                           0.76     58402\n",
      "   macro avg       0.78      0.61      0.62     58402\n",
      "weighted avg       0.77      0.76      0.71     58402\n",
      "\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, prediction)\n",
    "print(\"Final Result\")\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
    "print(\"Report:\")\n",
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.738593666129898\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "print(jaccard_score(y_test, prediction))"
   ]
  }
 ],
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