ml accidents.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'Road_Accidents.csv' does not exist: b'Road_Accidents.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-f291301f051c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Road_Accidents.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1120\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1124\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1855\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'Road_Accidents.csv' does not exist: b'Road_Accidents.csv'"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('Road_Accidents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 16)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "\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>Years</th>\n",
       "      <th>Total Number of Road Accidents (in numbers)</th>\n",
       "      <th>Total Number of Persons Killed (in numbers)</th>\n",
       "      <th>Total Number of Persons Injured (in numbers)</th>\n",
       "      <th>Population of India (in thousands)</th>\n",
       "      <th>Total Number of Registered Motor Vehicles (in thousands)</th>\n",
       "      <th>Road Length (in kms)</th>\n",
       "      <th>Number of Accidents per Lakh Population</th>\n",
       "      <th>Number of Accidents per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Accidents per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Killed Per Lakh Population</th>\n",
       "      <th>Number of Persons Killed Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Killed per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Injured per Lakh Population</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Kms of Roads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>114100</td>\n",
       "      <td>14500</td>\n",
       "      <td>70100</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>1188728.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>814.4</td>\n",
       "      <td>959.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>103.5</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>500.4</td>\n",
       "      <td>589.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>153200</td>\n",
       "      <td>24000</td>\n",
       "      <td>109100</td>\n",
       "      <td>673000.0</td>\n",
       "      <td>4521.0</td>\n",
       "      <td>1491873.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>338.9</td>\n",
       "      <td>1026.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>53.1</td>\n",
       "      <td>160.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>241.3</td>\n",
       "      <td>731.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>282600</td>\n",
       "      <td>54100</td>\n",
       "      <td>244100</td>\n",
       "      <td>835000.0</td>\n",
       "      <td>19152.0</td>\n",
       "      <td>1983867.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>147.6</td>\n",
       "      <td>1424.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>272.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>127.5</td>\n",
       "      <td>1230.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994</td>\n",
       "      <td>325864</td>\n",
       "      <td>64463</td>\n",
       "      <td>311500</td>\n",
       "      <td>904000.0</td>\n",
       "      <td>27660.0</td>\n",
       "      <td>2890950.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>117.8</td>\n",
       "      <td>1127.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>223.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>112.6</td>\n",
       "      <td>1077.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>351999</td>\n",
       "      <td>70781</td>\n",
       "      <td>323200</td>\n",
       "      <td>924359.0</td>\n",
       "      <td>30295.0</td>\n",
       "      <td>2975035.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>116.2</td>\n",
       "      <td>1183.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>23.4</td>\n",
       "      <td>237.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>1086.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1996</td>\n",
       "      <td>371204</td>\n",
       "      <td>74665</td>\n",
       "      <td>369502</td>\n",
       "      <td>941579.0</td>\n",
       "      <td>33786.0</td>\n",
       "      <td>3202515.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>109.9</td>\n",
       "      <td>1159.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.1</td>\n",
       "      <td>233.1</td>\n",
       "      <td>39.2</td>\n",
       "      <td>109.4</td>\n",
       "      <td>1153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1997</td>\n",
       "      <td>373671</td>\n",
       "      <td>76977</td>\n",
       "      <td>378361</td>\n",
       "      <td>959792.0</td>\n",
       "      <td>37332.0</td>\n",
       "      <td>3298788.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>100.1</td>\n",
       "      <td>1132.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>233.3</td>\n",
       "      <td>39.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1998</td>\n",
       "      <td>385018</td>\n",
       "      <td>79919</td>\n",
       "      <td>390674</td>\n",
       "      <td>978081.0</td>\n",
       "      <td>41368.0</td>\n",
       "      <td>3228356.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>93.1</td>\n",
       "      <td>1192.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>247.6</td>\n",
       "      <td>39.9</td>\n",
       "      <td>94.4</td>\n",
       "      <td>1210.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1999</td>\n",
       "      <td>386456</td>\n",
       "      <td>81966</td>\n",
       "      <td>375051</td>\n",
       "      <td>996130.0</td>\n",
       "      <td>44875.0</td>\n",
       "      <td>3296650.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>86.1</td>\n",
       "      <td>1172.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>18.3</td>\n",
       "      <td>248.6</td>\n",
       "      <td>37.7</td>\n",
       "      <td>83.6</td>\n",
       "      <td>1137.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000</td>\n",
       "      <td>391449</td>\n",
       "      <td>78911</td>\n",
       "      <td>399265</td>\n",
       "      <td>1014825.0</td>\n",
       "      <td>48857.0</td>\n",
       "      <td>3316078.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>80.1</td>\n",
       "      <td>1180.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>238.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>81.7</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2001</td>\n",
       "      <td>405637</td>\n",
       "      <td>80888</td>\n",
       "      <td>405216</td>\n",
       "      <td>1028610.0</td>\n",
       "      <td>54991.0</td>\n",
       "      <td>3373520.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>73.8</td>\n",
       "      <td>1202.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>239.8</td>\n",
       "      <td>39.4</td>\n",
       "      <td>73.7</td>\n",
       "      <td>1201.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2002</td>\n",
       "      <td>407497</td>\n",
       "      <td>84674</td>\n",
       "      <td>408711</td>\n",
       "      <td>1045547.0</td>\n",
       "      <td>58924.0</td>\n",
       "      <td>3426603.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>1189.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.4</td>\n",
       "      <td>247.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>1192.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2003</td>\n",
       "      <td>406726</td>\n",
       "      <td>85998</td>\n",
       "      <td>435122</td>\n",
       "      <td>1062388.0</td>\n",
       "      <td>67007.0</td>\n",
       "      <td>3528654.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>60.7</td>\n",
       "      <td>1152.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>243.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>64.9</td>\n",
       "      <td>1233.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2004</td>\n",
       "      <td>429910</td>\n",
       "      <td>92618</td>\n",
       "      <td>464521</td>\n",
       "      <td>1079117.0</td>\n",
       "      <td>72718.0</td>\n",
       "      <td>3621507.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>59.1</td>\n",
       "      <td>1187.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>255.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>1282.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2005</td>\n",
       "      <td>439255</td>\n",
       "      <td>94968</td>\n",
       "      <td>465282</td>\n",
       "      <td>1095722.0</td>\n",
       "      <td>81502.0</td>\n",
       "      <td>3809156.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>53.9</td>\n",
       "      <td>1153.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>249.3</td>\n",
       "      <td>42.5</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1221.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>460920</td>\n",
       "      <td>105749</td>\n",
       "      <td>496481</td>\n",
       "      <td>1112186.0</td>\n",
       "      <td>89618.0</td>\n",
       "      <td>3880651.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>51.4</td>\n",
       "      <td>1187.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>272.5</td>\n",
       "      <td>44.6</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1279.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2007</td>\n",
       "      <td>479216</td>\n",
       "      <td>114444</td>\n",
       "      <td>513340</td>\n",
       "      <td>1128521.0</td>\n",
       "      <td>96707.0</td>\n",
       "      <td>4016401.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>49.6</td>\n",
       "      <td>1193.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>284.9</td>\n",
       "      <td>45.5</td>\n",
       "      <td>53.1</td>\n",
       "      <td>1278.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008</td>\n",
       "      <td>484704</td>\n",
       "      <td>119860</td>\n",
       "      <td>523193</td>\n",
       "      <td>1144734.0</td>\n",
       "      <td>105353.0</td>\n",
       "      <td>4109592.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1179.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>11.4</td>\n",
       "      <td>291.7</td>\n",
       "      <td>45.7</td>\n",
       "      <td>49.7</td>\n",
       "      <td>1273.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2009</td>\n",
       "      <td>486384</td>\n",
       "      <td>125660</td>\n",
       "      <td>515458</td>\n",
       "      <td>1160813.0</td>\n",
       "      <td>114951.0</td>\n",
       "      <td>4471510.0</td>\n",
       "      <td>41.9</td>\n",
       "      <td>42.3</td>\n",
       "      <td>1087.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.9</td>\n",
       "      <td>281.0</td>\n",
       "      <td>44.4</td>\n",
       "      <td>44.8</td>\n",
       "      <td>1152.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2010</td>\n",
       "      <td>499628</td>\n",
       "      <td>134513</td>\n",
       "      <td>527512</td>\n",
       "      <td>1176742.0</td>\n",
       "      <td>127746.0</td>\n",
       "      <td>4582439.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>1090.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>293.5</td>\n",
       "      <td>44.8</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1151.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011</td>\n",
       "      <td>497686</td>\n",
       "      <td>142485</td>\n",
       "      <td>511394</td>\n",
       "      <td>1210193.4</td>\n",
       "      <td>141865.6</td>\n",
       "      <td>4676838.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1064.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>304.7</td>\n",
       "      <td>42.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1093.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012</td>\n",
       "      <td>490383</td>\n",
       "      <td>138258</td>\n",
       "      <td>509667</td>\n",
       "      <td>1208116.0</td>\n",
       "      <td>159490.6</td>\n",
       "      <td>4865394.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1007.9</td>\n",
       "      <td>11.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>284.2</td>\n",
       "      <td>42.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1047.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>486476</td>\n",
       "      <td>137572</td>\n",
       "      <td>494893</td>\n",
       "      <td>1223581.0</td>\n",
       "      <td>181508.0</td>\n",
       "      <td>5231922.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>26.8</td>\n",
       "      <td>929.8</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>262.9</td>\n",
       "      <td>40.4</td>\n",
       "      <td>27.3</td>\n",
       "      <td>945.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014</td>\n",
       "      <td>489400</td>\n",
       "      <td>139671</td>\n",
       "      <td>493474</td>\n",
       "      <td>1238887.0</td>\n",
       "      <td>190704.0</td>\n",
       "      <td>5402486.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>905.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>258.5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>25.9</td>\n",
       "      <td>913.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015</td>\n",
       "      <td>501423</td>\n",
       "      <td>146133</td>\n",
       "      <td>500279</td>\n",
       "      <td>1254019.0</td>\n",
       "      <td>210023.0</td>\n",
       "      <td>5472144.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>916.3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>914.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016</td>\n",
       "      <td>480652</td>\n",
       "      <td>150785</td>\n",
       "      <td>494624</td>\n",
       "      <td>1268961.0</td>\n",
       "      <td>230031.0</td>\n",
       "      <td>5603293.0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>20.9</td>\n",
       "      <td>857.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>269.1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>882.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017</td>\n",
       "      <td>464910</td>\n",
       "      <td>147913</td>\n",
       "      <td>470975</td>\n",
       "      <td>1283601.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Years  Total Number of Road Accidents (in numbers)  \\\n",
       "0    1970                                       114100   \n",
       "1    1980                                       153200   \n",
       "2    1990                                       282600   \n",
       "3    1994                                       325864   \n",
       "4    1995                                       351999   \n",
       "5    1996                                       371204   \n",
       "6    1997                                       373671   \n",
       "7    1998                                       385018   \n",
       "8    1999                                       386456   \n",
       "9    2000                                       391449   \n",
       "10   2001                                       405637   \n",
       "11   2002                                       407497   \n",
       "12   2003                                       406726   \n",
       "13   2004                                       429910   \n",
       "14   2005                                       439255   \n",
       "15   2006                                       460920   \n",
       "16   2007                                       479216   \n",
       "17   2008                                       484704   \n",
       "18   2009                                       486384   \n",
       "19   2010                                       499628   \n",
       "20   2011                                       497686   \n",
       "21   2012                                       490383   \n",
       "22   2013                                       486476   \n",
       "23   2014                                       489400   \n",
       "24   2015                                       501423   \n",
       "25   2016                                       480652   \n",
       "26   2017                                       464910   \n",
       "\n",
       "    Total Number of Persons Killed (in numbers)  \\\n",
       "0                                         14500   \n",
       "1                                         24000   \n",
       "2                                         54100   \n",
       "3                                         64463   \n",
       "4                                         70781   \n",
       "5                                         74665   \n",
       "6                                         76977   \n",
       "7                                         79919   \n",
       "8                                         81966   \n",
       "9                                         78911   \n",
       "10                                        80888   \n",
       "11                                        84674   \n",
       "12                                        85998   \n",
       "13                                        92618   \n",
       "14                                        94968   \n",
       "15                                       105749   \n",
       "16                                       114444   \n",
       "17                                       119860   \n",
       "18                                       125660   \n",
       "19                                       134513   \n",
       "20                                       142485   \n",
       "21                                       138258   \n",
       "22                                       137572   \n",
       "23                                       139671   \n",
       "24                                       146133   \n",
       "25                                       150785   \n",
       "26                                       147913   \n",
       "\n",
       "    Total Number of Persons Injured (in numbers)  \\\n",
       "0                                          70100   \n",
       "1                                         109100   \n",
       "2                                         244100   \n",
       "3                                         311500   \n",
       "4                                         323200   \n",
       "5                                         369502   \n",
       "6                                         378361   \n",
       "7                                         390674   \n",
       "8                                         375051   \n",
       "9                                         399265   \n",
       "10                                        405216   \n",
       "11                                        408711   \n",
       "12                                        435122   \n",
       "13                                        464521   \n",
       "14                                        465282   \n",
       "15                                        496481   \n",
       "16                                        513340   \n",
       "17                                        523193   \n",
       "18                                        515458   \n",
       "19                                        527512   \n",
       "20                                        511394   \n",
       "21                                        509667   \n",
       "22                                        494893   \n",
       "23                                        493474   \n",
       "24                                        500279   \n",
       "25                                        494624   \n",
       "26                                        470975   \n",
       "\n",
       "    Population of India (in thousands)  \\\n",
       "0                             539000.0   \n",
       "1                             673000.0   \n",
       "2                             835000.0   \n",
       "3                             904000.0   \n",
       "4                             924359.0   \n",
       "5                             941579.0   \n",
       "6                             959792.0   \n",
       "7                             978081.0   \n",
       "8                             996130.0   \n",
       "9                            1014825.0   \n",
       "10                           1028610.0   \n",
       "11                           1045547.0   \n",
       "12                           1062388.0   \n",
       "13                           1079117.0   \n",
       "14                           1095722.0   \n",
       "15                           1112186.0   \n",
       "16                           1128521.0   \n",
       "17                           1144734.0   \n",
       "18                           1160813.0   \n",
       "19                           1176742.0   \n",
       "20                           1210193.4   \n",
       "21                           1208116.0   \n",
       "22                           1223581.0   \n",
       "23                           1238887.0   \n",
       "24                           1254019.0   \n",
       "25                           1268961.0   \n",
       "26                           1283601.0   \n",
       "\n",
       "    Total Number of Registered Motor Vehicles (in thousands)  \\\n",
       "0                                              1401.0          \n",
       "1                                              4521.0          \n",
       "2                                             19152.0          \n",
       "3                                             27660.0          \n",
       "4                                             30295.0          \n",
       "5                                             33786.0          \n",
       "6                                             37332.0          \n",
       "7                                             41368.0          \n",
       "8                                             44875.0          \n",
       "9                                             48857.0          \n",
       "10                                            54991.0          \n",
       "11                                            58924.0          \n",
       "12                                            67007.0          \n",
       "13                                            72718.0          \n",
       "14                                            81502.0          \n",
       "15                                            89618.0          \n",
       "16                                            96707.0          \n",
       "17                                           105353.0          \n",
       "18                                           114951.0          \n",
       "19                                           127746.0          \n",
       "20                                           141865.6          \n",
       "21                                           159490.6          \n",
       "22                                           181508.0          \n",
       "23                                           190704.0          \n",
       "24                                           210023.0          \n",
       "25                                           230031.0          \n",
       "26                                                NaN          \n",
       "\n",
       "    Road Length (in kms)  Number of Accidents per Lakh Population  \\\n",
       "0              1188728.0                                     21.2   \n",
       "1              1491873.0                                     22.8   \n",
       "2              1983867.0                                     33.8   \n",
       "3              2890950.0                                     36.0   \n",
       "4              2975035.0                                     38.1   \n",
       "5              3202515.0                                     39.4   \n",
       "6              3298788.0                                     38.9   \n",
       "7              3228356.0                                     39.4   \n",
       "8              3296650.0                                     38.8   \n",
       "9              3316078.0                                     38.6   \n",
       "10             3373520.0                                     39.4   \n",
       "11             3426603.0                                     39.0   \n",
       "12             3528654.0                                     38.3   \n",
       "13             3621507.0                                     39.8   \n",
       "14             3809156.0                                     40.1   \n",
       "15             3880651.0                                     41.4   \n",
       "16             4016401.0                                     42.5   \n",
       "17             4109592.0                                     42.3   \n",
       "18             4471510.0                                     41.9   \n",
       "19             4582439.0                                     42.5   \n",
       "20             4676838.0                                     41.1   \n",
       "21             4865394.0                                     40.6   \n",
       "22             5231922.0                                     39.8   \n",
       "23             5402486.0                                     39.5   \n",
       "24             5472144.0                                     40.0   \n",
       "25             5603293.0                                     37.9   \n",
       "26                   NaN                                     36.2   \n",
       "\n",
       "    Number of Accidents per Ten Thousand Vehicles  \\\n",
       "0                                           814.4   \n",
       "1                                           338.9   \n",
       "2                                           147.6   \n",
       "3                                           117.8   \n",
       "4                                           116.2   \n",
       "5                                           109.9   \n",
       "6                                           100.1   \n",
       "7                                            93.1   \n",
       "8                                            86.1   \n",
       "9                                            80.1   \n",
       "10                                           73.8   \n",
       "11                                           69.2   \n",
       "12                                           60.7   \n",
       "13                                           59.1   \n",
       "14                                           53.9   \n",
       "15                                           51.4   \n",
       "16                                           49.6   \n",
       "17                                           46.0   \n",
       "18                                           42.3   \n",
       "19                                           39.1   \n",
       "20                                           35.1   \n",
       "21                                           30.7   \n",
       "22                                           26.8   \n",
       "23                                           25.7   \n",
       "24                                           23.9   \n",
       "25                                           20.9   \n",
       "26                                            NaN   \n",
       "\n",
       "    Number of Accidents per Ten Thousand Kms of Roads  \\\n",
       "0                                               959.8   \n",
       "1                                              1026.9   \n",
       "2                                              1424.5   \n",
       "3                                              1127.2   \n",
       "4                                              1183.2   \n",
       "5                                              1159.1   \n",
       "6                                              1132.8   \n",
       "7                                              1192.6   \n",
       "8                                              1172.3   \n",
       "9                                              1180.5   \n",
       "10                                             1202.4   \n",
       "11                                             1189.2   \n",
       "12                                             1152.6   \n",
       "13                                             1187.1   \n",
       "14                                             1153.2   \n",
       "15                                             1187.7   \n",
       "16                                             1193.1   \n",
       "17                                             1179.4   \n",
       "18                                             1087.7   \n",
       "19                                             1090.3   \n",
       "20                                             1064.2   \n",
       "21                                             1007.9   \n",
       "22                                              929.8   \n",
       "23                                              905.9   \n",
       "24                                              916.3   \n",
       "25                                              857.8   \n",
       "26                                                NaN   \n",
       "\n",
       "    Number of Persons Killed Per Lakh Population  \\\n",
       "0                                            2.7   \n",
       "1                                            3.6   \n",
       "2                                            6.5   \n",
       "3                                            7.1   \n",
       "4                                            7.7   \n",
       "5                                            7.9   \n",
       "6                                            8.0   \n",
       "7                                            8.2   \n",
       "8                                            8.2   \n",
       "9                                            7.8   \n",
       "10                                           7.9   \n",
       "11                                           8.1   \n",
       "12                                           8.1   \n",
       "13                                           8.6   \n",
       "14                                           8.7   \n",
       "15                                           9.5   \n",
       "16                                          10.1   \n",
       "17                                          10.5   \n",
       "18                                          10.8   \n",
       "19                                          11.4   \n",
       "20                                          11.8   \n",
       "21                                          11.4   \n",
       "22                                          11.2   \n",
       "23                                          11.3   \n",
       "24                                          11.7   \n",
       "25                                          11.9   \n",
       "26                                          11.5   \n",
       "\n",
       "    Number of Persons Killed Per Ten Thousand Vehicles  \\\n",
       "0                                               103.5    \n",
       "1                                                53.1    \n",
       "2                                                28.2    \n",
       "3                                                23.3    \n",
       "4                                                23.4    \n",
       "5                                                22.1    \n",
       "6                                                20.6    \n",
       "7                                                19.3    \n",
       "8                                                18.3    \n",
       "9                                                16.2    \n",
       "10                                               14.7    \n",
       "11                                               14.4    \n",
       "12                                               12.8    \n",
       "13                                               12.7    \n",
       "14                                               11.7    \n",
       "15                                               11.8    \n",
       "16                                               11.8    \n",
       "17                                               11.4    \n",
       "18                                               10.9    \n",
       "19                                               10.5    \n",
       "20                                               10.0    \n",
       "21                                                8.7    \n",
       "22                                                7.6    \n",
       "23                                                7.3    \n",
       "24                                                7.0    \n",
       "25                                                6.6    \n",
       "26                                                NaN    \n",
       "\n",
       "    Number of Persons Killed per Ten Thousand Kms of Roads  \\\n",
       "0                                               122.0        \n",
       "1                                               160.9        \n",
       "2                                               272.7        \n",
       "3                                               223.0        \n",
       "4                                               237.9        \n",
       "5                                               233.1        \n",
       "6                                               233.3        \n",
       "7                                               247.6        \n",
       "8                                               248.6        \n",
       "9                                               238.0        \n",
       "10                                              239.8        \n",
       "11                                              247.1        \n",
       "12                                              243.7        \n",
       "13                                              255.7        \n",
       "14                                              249.3        \n",
       "15                                              272.5        \n",
       "16                                              284.9        \n",
       "17                                              291.7        \n",
       "18                                              281.0        \n",
       "19                                              293.5        \n",
       "20                                              304.7        \n",
       "21                                              284.2        \n",
       "22                                              262.9        \n",
       "23                                              258.5        \n",
       "24                                              267.0        \n",
       "25                                              269.1        \n",
       "26                                                NaN        \n",
       "\n",
       "    Number of Persons Injured per Lakh Population  \\\n",
       "0                                            13.0   \n",
       "1                                            16.2   \n",
       "2                                            29.2   \n",
       "3                                            34.5   \n",
       "4                                            35.0   \n",
       "5                                            39.2   \n",
       "6                                            39.4   \n",
       "7                                            39.9   \n",
       "8                                            37.7   \n",
       "9                                            39.3   \n",
       "10                                           39.4   \n",
       "11                                           39.1   \n",
       "12                                           41.0   \n",
       "13                                           43.0   \n",
       "14                                           42.5   \n",
       "15                                           44.6   \n",
       "16                                           45.5   \n",
       "17                                           45.7   \n",
       "18                                           44.4   \n",
       "19                                           44.8   \n",
       "20                                           42.3   \n",
       "21                                           42.2   \n",
       "22                                           40.4   \n",
       "23                                           39.8   \n",
       "24                                           39.9   \n",
       "25                                           39.0   \n",
       "26                                           36.7   \n",
       "\n",
       "    Number of Persons Injured Per Ten Thousand Vehicles  \\\n",
       "0                                               500.4     \n",
       "1                                               241.3     \n",
       "2                                               127.5     \n",
       "3                                               112.6     \n",
       "4                                               106.7     \n",
       "5                                               109.4     \n",
       "6                                               101.4     \n",
       "7                                                94.4     \n",
       "8                                                83.6     \n",
       "9                                                81.7     \n",
       "10                                               73.7     \n",
       "11                                               69.4     \n",
       "12                                               64.9     \n",
       "13                                               63.9     \n",
       "14                                               57.1     \n",
       "15                                               55.4     \n",
       "16                                               53.1     \n",
       "17                                               49.7     \n",
       "18                                               44.8     \n",
       "19                                               41.3     \n",
       "20                                               36.0     \n",
       "21                                               32.0     \n",
       "22                                               27.3     \n",
       "23                                               25.9     \n",
       "24                                               23.8     \n",
       "25                                               21.5     \n",
       "26                                                NaN     \n",
       "\n",
       "    Number of Persons Injured Per Ten Thousand Kms of Roads  \n",
       "0                                               589.7        \n",
       "1                                               731.3        \n",
       "2                                              1230.4        \n",
       "3                                              1077.5        \n",
       "4                                              1086.4        \n",
       "5                                              1153.8        \n",
       "6                                              1147.0        \n",
       "7                                              1210.1        \n",
       "8                                              1137.7        \n",
       "9                                              1204.0        \n",
       "10                                             1201.2        \n",
       "11                                             1192.8        \n",
       "12                                             1233.1        \n",
       "13                                             1282.7        \n",
       "14                                             1221.5        \n",
       "15                                             1279.4        \n",
       "16                                             1278.1        \n",
       "17                                             1273.1        \n",
       "18                                             1152.8        \n",
       "19                                             1151.2        \n",
       "20                                             1093.5        \n",
       "21                                             1047.5        \n",
       "22                                              945.9        \n",
       "23                                              913.4        \n",
       "24                                              914.2        \n",
       "25                                              882.7        \n",
       "26                                                NaN        "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years                                                         int64\n",
       "Total Number of Road Accidents (in numbers)                   int64\n",
       "Total Number of Persons Killed (in numbers)                   int64\n",
       "Total Number of Persons Injured (in numbers)                  int64\n",
       "Population of India (in thousands)                          float64\n",
       "Total Number of Registered Motor Vehicles (in thousands)    float64\n",
       "Road Length (in kms)                                        float64\n",
       "Number of Accidents per Lakh Population                     float64\n",
       "Number of Accidents per Ten Thousand Vehicles               float64\n",
       "Number of Accidents per Ten Thousand Kms of Roads           float64\n",
       "Number of Persons Killed Per Lakh Population                float64\n",
       "Number of Persons Killed Per Ten Thousand Vehicles          float64\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads      float64\n",
       "Number of Persons Injured per Lakh Population               float64\n",
       "Number of Persons Injured Per Ten Thousand Vehicles         float64\n",
       "Number of Persons Injured Per Ten Thousand Kms of Roads     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3135561"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the total number of road accidents from 1970 to 2000\n",
    "\n",
    "data.iloc[:,1].loc[(data['Years']>=1970) & (data['Years']<=2000)].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2042189"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the total number of persons killed where number of road accidents is greater than 400000\n",
    "\n",
    "data.iloc[:,2].loc[(data['Total Number of Road Accidents (in numbers)'] > 400000)].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105749"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the percent of people killed in road accidents in the year 2006\n",
    "\n",
    "data.iloc[:,2].loc[(data['Years']==2006)].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.508211755947297"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_number_in_2006 = 105749\n",
    "total_number_in_pop = 1112186\n",
    "\n",
    "percent_of_people_killed= (total_number_in_2006/total_number_in_pop) * 100 \n",
    "\n",
    "percent_of_people_killed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years                                                       0\n",
       "Total Number of Road Accidents (in numbers)                 0\n",
       "Total Number of Persons Killed (in numbers)                 0\n",
       "Total Number of Persons Injured (in numbers)                0\n",
       "Population of India (in thousands)                          0\n",
       "Total Number of Registered Motor Vehicles (in thousands)    1\n",
       "Road Length (in kms)                                        1\n",
       "Number of Accidents per Lakh Population                     0\n",
       "Number of Accidents per Ten Thousand Vehicles               1\n",
       "Number of Accidents per Ten Thousand Kms of Roads           1\n",
       "Number of Persons Killed Per Lakh Population                0\n",
       "Number of Persons Killed Per Ten Thousand Vehicles          1\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads      1\n",
       "Number of Persons Injured per Lakh Population               0\n",
       "Number of Persons Injured Per Ten Thousand Vehicles         1\n",
       "Number of Persons Injured Per Ten Thousand Kms of Roads     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years                                                          2017.0\n",
       "Total Number of Road Accidents (in numbers)                  464910.0\n",
       "Total Number of Persons Killed (in numbers)                  147913.0\n",
       "Total Number of Persons Injured (in numbers)                 470975.0\n",
       "Population of India (in thousands)                          1283601.0\n",
       "Total Number of Registered Motor Vehicles (in thousands)          NaN\n",
       "Road Length (in kms)                                              NaN\n",
       "Number of Accidents per Lakh Population                          36.2\n",
       "Number of Accidents per Ten Thousand Vehicles                     NaN\n",
       "Number of Accidents per Ten Thousand Kms of Roads                 NaN\n",
       "Number of Persons Killed Per Lakh Population                     11.5\n",
       "Number of Persons Killed Per Ten Thousand Vehicles                NaN\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads            NaN\n",
       "Number of Persons Injured per Lakh Population                    36.7\n",
       "Number of Persons Injured Per Ten Thousand Vehicles               NaN\n",
       "Number of Persons Injured Per Ten Thousand Kms of Roads           NaN\n",
       "Name: 26, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.iloc[26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = data['Total Number of Registered Motor Vehicles (in thousands)'].median()\n",
    "data['Total Number of Registered Motor Vehicles (in thousands)'].fillna(m1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = data['Road Length (in kms)'].median()\n",
    "data['Road Length (in kms)'].fillna(m2,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "m3= data['Number of Accidents per Ten Thousand Vehicles'].median()\n",
    "data['Number of Accidents per Ten Thousand Vehicles'].fillna(m3,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "m4= data['Number of Accidents per Ten Thousand Kms of Roads'].median()\n",
    "\n",
    "data['Number of Accidents per Ten Thousand Kms of Roads'].fillna(m4,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "m5= data['Number of Persons Killed Per Ten Thousand Vehicles'].median()\n",
    "data['Number of Persons Killed Per Ten Thousand Vehicles'].fillna(m5,inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "m6= data['Number of Persons Killed per Ten Thousand Kms of Roads'].median()\n",
    "data['Number of Persons Killed per Ten Thousand Kms of Roads'].fillna(m6,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "m7= data['Number of Persons Injured Per Ten Thousand Vehicles'].median()\n",
    "data['Number of Persons Injured Per Ten Thousand Vehicles'].fillna(m7,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "m8= data['Number of Persons Injured Per Ten Thousand Kms of Roads'].median()\n",
    "data['Number of Persons Injured Per Ten Thousand Kms of Roads'].fillna(m8,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years                                                       0\n",
       "Total Number of Road Accidents (in numbers)                 0\n",
       "Total Number of Persons Killed (in numbers)                 0\n",
       "Total Number of Persons Injured (in numbers)                0\n",
       "Population of India (in thousands)                          0\n",
       "Total Number of Registered Motor Vehicles (in thousands)    0\n",
       "Road Length (in kms)                                        0\n",
       "Number of Accidents per Lakh Population                     0\n",
       "Number of Accidents per Ten Thousand Vehicles               0\n",
       "Number of Accidents per Ten Thousand Kms of Roads           0\n",
       "Number of Persons Killed Per Lakh Population                0\n",
       "Number of Persons Killed Per Ten Thousand Vehicles          0\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads      0\n",
       "Number of Persons Injured per Lakh Population               0\n",
       "Number of Persons Injured Per Ten Thousand Vehicles         0\n",
       "Number of Persons Injured Per Ten Thousand Kms of Roads     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 16)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27 entries, 0 to 26\n",
      "Data columns (total 16 columns):\n",
      "Years                                                       27 non-null int64\n",
      "Total Number of Road Accidents (in numbers)                 27 non-null int64\n",
      "Total Number of Persons Killed (in numbers)                 27 non-null int64\n",
      "Total Number of Persons Injured (in numbers)                27 non-null int64\n",
      "Population of India (in thousands)                          27 non-null float64\n",
      "Total Number of Registered Motor Vehicles (in thousands)    27 non-null float64\n",
      "Road Length (in kms)                                        27 non-null float64\n",
      "Number of Accidents per Lakh Population                     27 non-null float64\n",
      "Number of Accidents per Ten Thousand Vehicles               27 non-null float64\n",
      "Number of Accidents per Ten Thousand Kms of Roads           27 non-null float64\n",
      "Number of Persons Killed Per Lakh Population                27 non-null float64\n",
      "Number of Persons Killed Per Ten Thousand Vehicles          27 non-null float64\n",
      "Number of Persons Killed per Ten Thousand Kms of Roads      27 non-null float64\n",
      "Number of Persons Injured per Lakh Population               27 non-null float64\n",
      "Number of Persons Injured Per Ten Thousand Vehicles         27 non-null float64\n",
      "Number of Persons Injured Per Ten Thousand Kms of Roads     27 non-null float64\n",
      "dtypes: float64(12), int64(4)\n",
      "memory usage: 3.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Years</th>\n",
       "      <th>Total Number of Road Accidents (in numbers)</th>\n",
       "      <th>Total Number of Persons Killed (in numbers)</th>\n",
       "      <th>Total Number of Persons Injured (in numbers)</th>\n",
       "      <th>Population of India (in thousands)</th>\n",
       "      <th>Total Number of Registered Motor Vehicles (in thousands)</th>\n",
       "      <th>Road Length (in kms)</th>\n",
       "      <th>Number of Accidents per Lakh Population</th>\n",
       "      <th>Number of Accidents per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Accidents per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Killed Per Lakh Population</th>\n",
       "      <th>Number of Persons Killed Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Killed per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Injured per Lakh Population</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Kms of Roads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2002.666667</td>\n",
       "      <td>409124.740741</td>\n",
       "      <td>98610.037037</td>\n",
       "      <td>414851.666667</td>\n",
       "      <td>1.055093e+06</td>\n",
       "      <td>86749.951852</td>\n",
       "      <td>3.722964e+06</td>\n",
       "      <td>38.122222</td>\n",
       "      <td>102.677778</td>\n",
       "      <td>1111.718519</td>\n",
       "      <td>8.970370</td>\n",
       "      <td>18.912963</td>\n",
       "      <td>250.933333</td>\n",
       "      <td>38.285185</td>\n",
       "      <td>87.525926</td>\n",
       "      <td>1103.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.891069</td>\n",
       "      <td>99090.505944</td>\n",
       "      <td>37324.713290</td>\n",
       "      <td>119381.716096</td>\n",
       "      <td>1.783049e+05</td>\n",
       "      <td>63622.215611</td>\n",
       "      <td>1.117582e+06</td>\n",
       "      <td>5.060506</td>\n",
       "      <td>155.193419</td>\n",
       "      <td>121.726598</td>\n",
       "      <td>2.375972</td>\n",
       "      <td>19.322235</td>\n",
       "      <td>38.214063</td>\n",
       "      <td>7.747640</td>\n",
       "      <td>93.996201</td>\n",
       "      <td>172.431238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>114100.000000</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>70100.000000</td>\n",
       "      <td>5.390000e+05</td>\n",
       "      <td>1401.000000</td>\n",
       "      <td>1.188728e+06</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>857.800000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>589.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1997.500000</td>\n",
       "      <td>379344.500000</td>\n",
       "      <td>77944.000000</td>\n",
       "      <td>376706.000000</td>\n",
       "      <td>9.689365e+05</td>\n",
       "      <td>39350.000000</td>\n",
       "      <td>3.262503e+06</td>\n",
       "      <td>38.200000</td>\n",
       "      <td>40.700000</td>\n",
       "      <td>1045.550000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>238.900000</td>\n",
       "      <td>38.350000</td>\n",
       "      <td>43.050000</td>\n",
       "      <td>1062.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>429910.000000</td>\n",
       "      <td>92618.000000</td>\n",
       "      <td>464521.000000</td>\n",
       "      <td>1.079117e+06</td>\n",
       "      <td>69862.500000</td>\n",
       "      <td>3.575080e+06</td>\n",
       "      <td>39.400000</td>\n",
       "      <td>59.900000</td>\n",
       "      <td>1152.900000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>64.400000</td>\n",
       "      <td>1152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.500000</td>\n",
       "      <td>485544.000000</td>\n",
       "      <td>136042.500000</td>\n",
       "      <td>498380.000000</td>\n",
       "      <td>1.192429e+06</td>\n",
       "      <td>121348.500000</td>\n",
       "      <td>4.526974e+06</td>\n",
       "      <td>40.350000</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>1185.150000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>19.950000</td>\n",
       "      <td>272.600000</td>\n",
       "      <td>42.400000</td>\n",
       "      <td>97.900000</td>\n",
       "      <td>1215.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>501423.000000</td>\n",
       "      <td>150785.000000</td>\n",
       "      <td>527512.000000</td>\n",
       "      <td>1.283601e+06</td>\n",
       "      <td>230031.000000</td>\n",
       "      <td>5.603293e+06</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>814.400000</td>\n",
       "      <td>1424.500000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>304.700000</td>\n",
       "      <td>45.700000</td>\n",
       "      <td>500.400000</td>\n",
       "      <td>1282.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Years  Total Number of Road Accidents (in numbers)  \\\n",
       "count    27.000000                                    27.000000   \n",
       "mean   2002.666667                                409124.740741   \n",
       "std      10.891069                                 99090.505944   \n",
       "min    1970.000000                                114100.000000   \n",
       "25%    1997.500000                                379344.500000   \n",
       "50%    2004.000000                                429910.000000   \n",
       "75%    2010.500000                                485544.000000   \n",
       "max    2017.000000                                501423.000000   \n",
       "\n",
       "       Total Number of Persons Killed (in numbers)  \\\n",
       "count                                    27.000000   \n",
       "mean                                  98610.037037   \n",
       "std                                   37324.713290   \n",
       "min                                   14500.000000   \n",
       "25%                                   77944.000000   \n",
       "50%                                   92618.000000   \n",
       "75%                                  136042.500000   \n",
       "max                                  150785.000000   \n",
       "\n",
       "       Total Number of Persons Injured (in numbers)  \\\n",
       "count                                     27.000000   \n",
       "mean                                  414851.666667   \n",
       "std                                   119381.716096   \n",
       "min                                    70100.000000   \n",
       "25%                                   376706.000000   \n",
       "50%                                   464521.000000   \n",
       "75%                                   498380.000000   \n",
       "max                                   527512.000000   \n",
       "\n",
       "       Population of India (in thousands)  \\\n",
       "count                        2.700000e+01   \n",
       "mean                         1.055093e+06   \n",
       "std                          1.783049e+05   \n",
       "min                          5.390000e+05   \n",
       "25%                          9.689365e+05   \n",
       "50%                          1.079117e+06   \n",
       "75%                          1.192429e+06   \n",
       "max                          1.283601e+06   \n",
       "\n",
       "       Total Number of Registered Motor Vehicles (in thousands)  \\\n",
       "count                                          27.000000          \n",
       "mean                                        86749.951852          \n",
       "std                                         63622.215611          \n",
       "min                                          1401.000000          \n",
       "25%                                         39350.000000          \n",
       "50%                                         69862.500000          \n",
       "75%                                        121348.500000          \n",
       "max                                        230031.000000          \n",
       "\n",
       "       Road Length (in kms)  Number of Accidents per Lakh Population  \\\n",
       "count          2.700000e+01                                27.000000   \n",
       "mean           3.722964e+06                                38.122222   \n",
       "std            1.117582e+06                                 5.060506   \n",
       "min            1.188728e+06                                21.200000   \n",
       "25%            3.262503e+06                                38.200000   \n",
       "50%            3.575080e+06                                39.400000   \n",
       "75%            4.526974e+06                                40.350000   \n",
       "max            5.603293e+06                                42.500000   \n",
       "\n",
       "       Number of Accidents per Ten Thousand Vehicles  \\\n",
       "count                                      27.000000   \n",
       "mean                                      102.677778   \n",
       "std                                       155.193419   \n",
       "min                                        20.900000   \n",
       "25%                                        40.700000   \n",
       "50%                                        59.900000   \n",
       "75%                                        96.600000   \n",
       "max                                       814.400000   \n",
       "\n",
       "       Number of Accidents per Ten Thousand Kms of Roads  \\\n",
       "count                                          27.000000   \n",
       "mean                                         1111.718519   \n",
       "std                                           121.726598   \n",
       "min                                           857.800000   \n",
       "25%                                          1045.550000   \n",
       "50%                                          1152.900000   \n",
       "75%                                          1185.150000   \n",
       "max                                          1424.500000   \n",
       "\n",
       "       Number of Persons Killed Per Lakh Population  \\\n",
       "count                                     27.000000   \n",
       "mean                                       8.970370   \n",
       "std                                        2.375972   \n",
       "min                                        2.700000   \n",
       "25%                                        7.900000   \n",
       "50%                                        8.600000   \n",
       "75%                                       11.250000   \n",
       "max                                       11.900000   \n",
       "\n",
       "       Number of Persons Killed Per Ten Thousand Vehicles  \\\n",
       "count                                          27.000000    \n",
       "mean                                           18.912963    \n",
       "std                                            19.322235    \n",
       "min                                             6.600000    \n",
       "25%                                            10.700000    \n",
       "50%                                            12.750000    \n",
       "75%                                            19.950000    \n",
       "max                                           103.500000    \n",
       "\n",
       "       Number of Persons Killed per Ten Thousand Kms of Roads  \\\n",
       "count                                          27.000000        \n",
       "mean                                          250.933333        \n",
       "std                                            38.214063        \n",
       "min                                           122.000000        \n",
       "25%                                           238.900000        \n",
       "50%                                           252.500000        \n",
       "75%                                           272.600000        \n",
       "max                                           304.700000        \n",
       "\n",
       "       Number of Persons Injured per Lakh Population  \\\n",
       "count                                      27.000000   \n",
       "mean                                       38.285185   \n",
       "std                                         7.747640   \n",
       "min                                        13.000000   \n",
       "25%                                        38.350000   \n",
       "50%                                        39.800000   \n",
       "75%                                        42.400000   \n",
       "max                                        45.700000   \n",
       "\n",
       "       Number of Persons Injured Per Ten Thousand Vehicles  \\\n",
       "count                                          27.000000     \n",
       "mean                                           87.525926     \n",
       "std                                            93.996201     \n",
       "min                                            21.500000     \n",
       "25%                                            43.050000     \n",
       "50%                                            64.400000     \n",
       "75%                                            97.900000     \n",
       "max                                           500.400000     \n",
       "\n",
       "       Number of Persons Injured Per Ten Thousand Kms of Roads  \n",
       "count                                          27.000000        \n",
       "mean                                         1103.074074        \n",
       "std                                           172.431238        \n",
       "min                                           589.700000        \n",
       "25%                                          1062.500000        \n",
       "50%                                          1152.000000        \n",
       "75%                                          1215.800000        \n",
       "max                                          1282.700000        "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Years</th>\n",
       "      <th>Total Number of Road Accidents (in numbers)</th>\n",
       "      <th>Total Number of Persons Killed (in numbers)</th>\n",
       "      <th>Total Number of Persons Injured (in numbers)</th>\n",
       "      <th>Population of India (in thousands)</th>\n",
       "      <th>Total Number of Registered Motor Vehicles (in thousands)</th>\n",
       "      <th>Road Length (in kms)</th>\n",
       "      <th>Number of Accidents per Lakh Population</th>\n",
       "      <th>Number of Accidents per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Accidents per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Killed Per Lakh Population</th>\n",
       "      <th>Number of Persons Killed Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Killed per Ten Thousand Kms of Roads</th>\n",
       "      <th>Number of Persons Injured per Lakh Population</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Vehicles</th>\n",
       "      <th>Number of Persons Injured Per Ten Thousand Kms of Roads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>114100</td>\n",
       "      <td>14500</td>\n",
       "      <td>70100</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>1188728.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>814.4</td>\n",
       "      <td>959.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>103.50</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>500.4</td>\n",
       "      <td>589.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>153200</td>\n",
       "      <td>24000</td>\n",
       "      <td>109100</td>\n",
       "      <td>673000.0</td>\n",
       "      <td>4521.0</td>\n",
       "      <td>1491873.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>338.9</td>\n",
       "      <td>1026.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>53.10</td>\n",
       "      <td>160.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>241.3</td>\n",
       "      <td>731.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>282600</td>\n",
       "      <td>54100</td>\n",
       "      <td>244100</td>\n",
       "      <td>835000.0</td>\n",
       "      <td>19152.0</td>\n",
       "      <td>1983867.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>147.6</td>\n",
       "      <td>1424.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>28.20</td>\n",
       "      <td>272.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>127.5</td>\n",
       "      <td>1230.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994</td>\n",
       "      <td>325864</td>\n",
       "      <td>64463</td>\n",
       "      <td>311500</td>\n",
       "      <td>904000.0</td>\n",
       "      <td>27660.0</td>\n",
       "      <td>2890950.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>117.8</td>\n",
       "      <td>1127.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>23.30</td>\n",
       "      <td>223.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>112.6</td>\n",
       "      <td>1077.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>351999</td>\n",
       "      <td>70781</td>\n",
       "      <td>323200</td>\n",
       "      <td>924359.0</td>\n",
       "      <td>30295.0</td>\n",
       "      <td>2975035.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>116.2</td>\n",
       "      <td>1183.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>23.40</td>\n",
       "      <td>237.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>1086.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1996</td>\n",
       "      <td>371204</td>\n",
       "      <td>74665</td>\n",
       "      <td>369502</td>\n",
       "      <td>941579.0</td>\n",
       "      <td>33786.0</td>\n",
       "      <td>3202515.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>109.9</td>\n",
       "      <td>1159.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.10</td>\n",
       "      <td>233.1</td>\n",
       "      <td>39.2</td>\n",
       "      <td>109.4</td>\n",
       "      <td>1153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1997</td>\n",
       "      <td>373671</td>\n",
       "      <td>76977</td>\n",
       "      <td>378361</td>\n",
       "      <td>959792.0</td>\n",
       "      <td>37332.0</td>\n",
       "      <td>3298788.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>100.1</td>\n",
       "      <td>1132.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.60</td>\n",
       "      <td>233.3</td>\n",
       "      <td>39.4</td>\n",
       "      <td>101.4</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1998</td>\n",
       "      <td>385018</td>\n",
       "      <td>79919</td>\n",
       "      <td>390674</td>\n",
       "      <td>978081.0</td>\n",
       "      <td>41368.0</td>\n",
       "      <td>3228356.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>93.1</td>\n",
       "      <td>1192.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.30</td>\n",
       "      <td>247.6</td>\n",
       "      <td>39.9</td>\n",
       "      <td>94.4</td>\n",
       "      <td>1210.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1999</td>\n",
       "      <td>386456</td>\n",
       "      <td>81966</td>\n",
       "      <td>375051</td>\n",
       "      <td>996130.0</td>\n",
       "      <td>44875.0</td>\n",
       "      <td>3296650.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>86.1</td>\n",
       "      <td>1172.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>18.30</td>\n",
       "      <td>248.6</td>\n",
       "      <td>37.7</td>\n",
       "      <td>83.6</td>\n",
       "      <td>1137.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000</td>\n",
       "      <td>391449</td>\n",
       "      <td>78911</td>\n",
       "      <td>399265</td>\n",
       "      <td>1014825.0</td>\n",
       "      <td>48857.0</td>\n",
       "      <td>3316078.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>80.1</td>\n",
       "      <td>1180.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>16.20</td>\n",
       "      <td>238.0</td>\n",
       "      <td>39.3</td>\n",
       "      <td>81.7</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2001</td>\n",
       "      <td>405637</td>\n",
       "      <td>80888</td>\n",
       "      <td>405216</td>\n",
       "      <td>1028610.0</td>\n",
       "      <td>54991.0</td>\n",
       "      <td>3373520.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>73.8</td>\n",
       "      <td>1202.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>14.70</td>\n",
       "      <td>239.8</td>\n",
       "      <td>39.4</td>\n",
       "      <td>73.7</td>\n",
       "      <td>1201.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2002</td>\n",
       "      <td>407497</td>\n",
       "      <td>84674</td>\n",
       "      <td>408711</td>\n",
       "      <td>1045547.0</td>\n",
       "      <td>58924.0</td>\n",
       "      <td>3426603.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>1189.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.40</td>\n",
       "      <td>247.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>1192.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2003</td>\n",
       "      <td>406726</td>\n",
       "      <td>85998</td>\n",
       "      <td>435122</td>\n",
       "      <td>1062388.0</td>\n",
       "      <td>67007.0</td>\n",
       "      <td>3528654.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>60.7</td>\n",
       "      <td>1152.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>12.80</td>\n",
       "      <td>243.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>64.9</td>\n",
       "      <td>1233.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2004</td>\n",
       "      <td>429910</td>\n",
       "      <td>92618</td>\n",
       "      <td>464521</td>\n",
       "      <td>1079117.0</td>\n",
       "      <td>72718.0</td>\n",
       "      <td>3621507.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>59.1</td>\n",
       "      <td>1187.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12.70</td>\n",
       "      <td>255.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>1282.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2005</td>\n",
       "      <td>439255</td>\n",
       "      <td>94968</td>\n",
       "      <td>465282</td>\n",
       "      <td>1095722.0</td>\n",
       "      <td>81502.0</td>\n",
       "      <td>3809156.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>53.9</td>\n",
       "      <td>1153.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>11.70</td>\n",
       "      <td>249.3</td>\n",
       "      <td>42.5</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1221.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>460920</td>\n",
       "      <td>105749</td>\n",
       "      <td>496481</td>\n",
       "      <td>1112186.0</td>\n",
       "      <td>89618.0</td>\n",
       "      <td>3880651.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>51.4</td>\n",
       "      <td>1187.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.80</td>\n",
       "      <td>272.5</td>\n",
       "      <td>44.6</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1279.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2007</td>\n",
       "      <td>479216</td>\n",
       "      <td>114444</td>\n",
       "      <td>513340</td>\n",
       "      <td>1128521.0</td>\n",
       "      <td>96707.0</td>\n",
       "      <td>4016401.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>49.6</td>\n",
       "      <td>1193.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>11.80</td>\n",
       "      <td>284.9</td>\n",
       "      <td>45.5</td>\n",
       "      <td>53.1</td>\n",
       "      <td>1278.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008</td>\n",
       "      <td>484704</td>\n",
       "      <td>119860</td>\n",
       "      <td>523193</td>\n",
       "      <td>1144734.0</td>\n",
       "      <td>105353.0</td>\n",
       "      <td>4109592.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1179.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>11.40</td>\n",
       "      <td>291.7</td>\n",
       "      <td>45.7</td>\n",
       "      <td>49.7</td>\n",
       "      <td>1273.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2009</td>\n",
       "      <td>486384</td>\n",
       "      <td>125660</td>\n",
       "      <td>515458</td>\n",
       "      <td>1160813.0</td>\n",
       "      <td>114951.0</td>\n",
       "      <td>4471510.0</td>\n",
       "      <td>41.9</td>\n",
       "      <td>42.3</td>\n",
       "      <td>1087.7</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.90</td>\n",
       "      <td>281.0</td>\n",
       "      <td>44.4</td>\n",
       "      <td>44.8</td>\n",
       "      <td>1152.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2010</td>\n",
       "      <td>499628</td>\n",
       "      <td>134513</td>\n",
       "      <td>527512</td>\n",
       "      <td>1176742.0</td>\n",
       "      <td>127746.0</td>\n",
       "      <td>4582439.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>1090.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>10.50</td>\n",
       "      <td>293.5</td>\n",
       "      <td>44.8</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1151.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2011</td>\n",
       "      <td>497686</td>\n",
       "      <td>142485</td>\n",
       "      <td>511394</td>\n",
       "      <td>1210193.4</td>\n",
       "      <td>141865.6</td>\n",
       "      <td>4676838.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1064.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.00</td>\n",
       "      <td>304.7</td>\n",
       "      <td>42.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1093.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012</td>\n",
       "      <td>490383</td>\n",
       "      <td>138258</td>\n",
       "      <td>509667</td>\n",
       "      <td>1208116.0</td>\n",
       "      <td>159490.6</td>\n",
       "      <td>4865394.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1007.9</td>\n",
       "      <td>11.4</td>\n",
       "      <td>8.70</td>\n",
       "      <td>284.2</td>\n",
       "      <td>42.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1047.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>486476</td>\n",
       "      <td>137572</td>\n",
       "      <td>494893</td>\n",
       "      <td>1223581.0</td>\n",
       "      <td>181508.0</td>\n",
       "      <td>5231922.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>26.8</td>\n",
       "      <td>929.8</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.60</td>\n",
       "      <td>262.9</td>\n",
       "      <td>40.4</td>\n",
       "      <td>27.3</td>\n",
       "      <td>945.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014</td>\n",
       "      <td>489400</td>\n",
       "      <td>139671</td>\n",
       "      <td>493474</td>\n",
       "      <td>1238887.0</td>\n",
       "      <td>190704.0</td>\n",
       "      <td>5402486.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>905.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>7.30</td>\n",
       "      <td>258.5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>25.9</td>\n",
       "      <td>913.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015</td>\n",
       "      <td>501423</td>\n",
       "      <td>146133</td>\n",
       "      <td>500279</td>\n",
       "      <td>1254019.0</td>\n",
       "      <td>210023.0</td>\n",
       "      <td>5472144.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>916.3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.00</td>\n",
       "      <td>267.0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>914.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016</td>\n",
       "      <td>480652</td>\n",
       "      <td>150785</td>\n",
       "      <td>494624</td>\n",
       "      <td>1268961.0</td>\n",
       "      <td>230031.0</td>\n",
       "      <td>5603293.0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>20.9</td>\n",
       "      <td>857.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>6.60</td>\n",
       "      <td>269.1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>882.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017</td>\n",
       "      <td>464910</td>\n",
       "      <td>147913</td>\n",
       "      <td>470975</td>\n",
       "      <td>1283601.0</td>\n",
       "      <td>69862.5</td>\n",
       "      <td>3575080.5</td>\n",
       "      <td>36.2</td>\n",
       "      <td>59.9</td>\n",
       "      <td>1152.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.75</td>\n",
       "      <td>252.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>64.4</td>\n",
       "      <td>1152.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Years  Total Number of Road Accidents (in numbers)  \\\n",
       "0    1970                                       114100   \n",
       "1    1980                                       153200   \n",
       "2    1990                                       282600   \n",
       "3    1994                                       325864   \n",
       "4    1995                                       351999   \n",
       "5    1996                                       371204   \n",
       "6    1997                                       373671   \n",
       "7    1998                                       385018   \n",
       "8    1999                                       386456   \n",
       "9    2000                                       391449   \n",
       "10   2001                                       405637   \n",
       "11   2002                                       407497   \n",
       "12   2003                                       406726   \n",
       "13   2004                                       429910   \n",
       "14   2005                                       439255   \n",
       "15   2006                                       460920   \n",
       "16   2007                                       479216   \n",
       "17   2008                                       484704   \n",
       "18   2009                                       486384   \n",
       "19   2010                                       499628   \n",
       "20   2011                                       497686   \n",
       "21   2012                                       490383   \n",
       "22   2013                                       486476   \n",
       "23   2014                                       489400   \n",
       "24   2015                                       501423   \n",
       "25   2016                                       480652   \n",
       "26   2017                                       464910   \n",
       "\n",
       "    Total Number of Persons Killed (in numbers)  \\\n",
       "0                                         14500   \n",
       "1                                         24000   \n",
       "2                                         54100   \n",
       "3                                         64463   \n",
       "4                                         70781   \n",
       "5                                         74665   \n",
       "6                                         76977   \n",
       "7                                         79919   \n",
       "8                                         81966   \n",
       "9                                         78911   \n",
       "10                                        80888   \n",
       "11                                        84674   \n",
       "12                                        85998   \n",
       "13                                        92618   \n",
       "14                                        94968   \n",
       "15                                       105749   \n",
       "16                                       114444   \n",
       "17                                       119860   \n",
       "18                                       125660   \n",
       "19                                       134513   \n",
       "20                                       142485   \n",
       "21                                       138258   \n",
       "22                                       137572   \n",
       "23                                       139671   \n",
       "24                                       146133   \n",
       "25                                       150785   \n",
       "26                                       147913   \n",
       "\n",
       "    Total Number of Persons Injured (in numbers)  \\\n",
       "0                                          70100   \n",
       "1                                         109100   \n",
       "2                                         244100   \n",
       "3                                         311500   \n",
       "4                                         323200   \n",
       "5                                         369502   \n",
       "6                                         378361   \n",
       "7                                         390674   \n",
       "8                                         375051   \n",
       "9                                         399265   \n",
       "10                                        405216   \n",
       "11                                        408711   \n",
       "12                                        435122   \n",
       "13                                        464521   \n",
       "14                                        465282   \n",
       "15                                        496481   \n",
       "16                                        513340   \n",
       "17                                        523193   \n",
       "18                                        515458   \n",
       "19                                        527512   \n",
       "20                                        511394   \n",
       "21                                        509667   \n",
       "22                                        494893   \n",
       "23                                        493474   \n",
       "24                                        500279   \n",
       "25                                        494624   \n",
       "26                                        470975   \n",
       "\n",
       "    Population of India (in thousands)  \\\n",
       "0                             539000.0   \n",
       "1                             673000.0   \n",
       "2                             835000.0   \n",
       "3                             904000.0   \n",
       "4                             924359.0   \n",
       "5                             941579.0   \n",
       "6                             959792.0   \n",
       "7                             978081.0   \n",
       "8                             996130.0   \n",
       "9                            1014825.0   \n",
       "10                           1028610.0   \n",
       "11                           1045547.0   \n",
       "12                           1062388.0   \n",
       "13                           1079117.0   \n",
       "14                           1095722.0   \n",
       "15                           1112186.0   \n",
       "16                           1128521.0   \n",
       "17                           1144734.0   \n",
       "18                           1160813.0   \n",
       "19                           1176742.0   \n",
       "20                           1210193.4   \n",
       "21                           1208116.0   \n",
       "22                           1223581.0   \n",
       "23                           1238887.0   \n",
       "24                           1254019.0   \n",
       "25                           1268961.0   \n",
       "26                           1283601.0   \n",
       "\n",
       "    Total Number of Registered Motor Vehicles (in thousands)  \\\n",
       "0                                              1401.0          \n",
       "1                                              4521.0          \n",
       "2                                             19152.0          \n",
       "3                                             27660.0          \n",
       "4                                             30295.0          \n",
       "5                                             33786.0          \n",
       "6                                             37332.0          \n",
       "7                                             41368.0          \n",
       "8                                             44875.0          \n",
       "9                                             48857.0          \n",
       "10                                            54991.0          \n",
       "11                                            58924.0          \n",
       "12                                            67007.0          \n",
       "13                                            72718.0          \n",
       "14                                            81502.0          \n",
       "15                                            89618.0          \n",
       "16                                            96707.0          \n",
       "17                                           105353.0          \n",
       "18                                           114951.0          \n",
       "19                                           127746.0          \n",
       "20                                           141865.6          \n",
       "21                                           159490.6          \n",
       "22                                           181508.0          \n",
       "23                                           190704.0          \n",
       "24                                           210023.0          \n",
       "25                                           230031.0          \n",
       "26                                            69862.5          \n",
       "\n",
       "    Road Length (in kms)  Number of Accidents per Lakh Population  \\\n",
       "0              1188728.0                                     21.2   \n",
       "1              1491873.0                                     22.8   \n",
       "2              1983867.0                                     33.8   \n",
       "3              2890950.0                                     36.0   \n",
       "4              2975035.0                                     38.1   \n",
       "5              3202515.0                                     39.4   \n",
       "6              3298788.0                                     38.9   \n",
       "7              3228356.0                                     39.4   \n",
       "8              3296650.0                                     38.8   \n",
       "9              3316078.0                                     38.6   \n",
       "10             3373520.0                                     39.4   \n",
       "11             3426603.0                                     39.0   \n",
       "12             3528654.0                                     38.3   \n",
       "13             3621507.0                                     39.8   \n",
       "14             3809156.0                                     40.1   \n",
       "15             3880651.0                                     41.4   \n",
       "16             4016401.0                                     42.5   \n",
       "17             4109592.0                                     42.3   \n",
       "18             4471510.0                                     41.9   \n",
       "19             4582439.0                                     42.5   \n",
       "20             4676838.0                                     41.1   \n",
       "21             4865394.0                                     40.6   \n",
       "22             5231922.0                                     39.8   \n",
       "23             5402486.0                                     39.5   \n",
       "24             5472144.0                                     40.0   \n",
       "25             5603293.0                                     37.9   \n",
       "26             3575080.5                                     36.2   \n",
       "\n",
       "    Number of Accidents per Ten Thousand Vehicles  \\\n",
       "0                                           814.4   \n",
       "1                                           338.9   \n",
       "2                                           147.6   \n",
       "3                                           117.8   \n",
       "4                                           116.2   \n",
       "5                                           109.9   \n",
       "6                                           100.1   \n",
       "7                                            93.1   \n",
       "8                                            86.1   \n",
       "9                                            80.1   \n",
       "10                                           73.8   \n",
       "11                                           69.2   \n",
       "12                                           60.7   \n",
       "13                                           59.1   \n",
       "14                                           53.9   \n",
       "15                                           51.4   \n",
       "16                                           49.6   \n",
       "17                                           46.0   \n",
       "18                                           42.3   \n",
       "19                                           39.1   \n",
       "20                                           35.1   \n",
       "21                                           30.7   \n",
       "22                                           26.8   \n",
       "23                                           25.7   \n",
       "24                                           23.9   \n",
       "25                                           20.9   \n",
       "26                                           59.9   \n",
       "\n",
       "    Number of Accidents per Ten Thousand Kms of Roads  \\\n",
       "0                                               959.8   \n",
       "1                                              1026.9   \n",
       "2                                              1424.5   \n",
       "3                                              1127.2   \n",
       "4                                              1183.2   \n",
       "5                                              1159.1   \n",
       "6                                              1132.8   \n",
       "7                                              1192.6   \n",
       "8                                              1172.3   \n",
       "9                                              1180.5   \n",
       "10                                             1202.4   \n",
       "11                                             1189.2   \n",
       "12                                             1152.6   \n",
       "13                                             1187.1   \n",
       "14                                             1153.2   \n",
       "15                                             1187.7   \n",
       "16                                             1193.1   \n",
       "17                                             1179.4   \n",
       "18                                             1087.7   \n",
       "19                                             1090.3   \n",
       "20                                             1064.2   \n",
       "21                                             1007.9   \n",
       "22                                              929.8   \n",
       "23                                              905.9   \n",
       "24                                              916.3   \n",
       "25                                              857.8   \n",
       "26                                             1152.9   \n",
       "\n",
       "    Number of Persons Killed Per Lakh Population  \\\n",
       "0                                            2.7   \n",
       "1                                            3.6   \n",
       "2                                            6.5   \n",
       "3                                            7.1   \n",
       "4                                            7.7   \n",
       "5                                            7.9   \n",
       "6                                            8.0   \n",
       "7                                            8.2   \n",
       "8                                            8.2   \n",
       "9                                            7.8   \n",
       "10                                           7.9   \n",
       "11                                           8.1   \n",
       "12                                           8.1   \n",
       "13                                           8.6   \n",
       "14                                           8.7   \n",
       "15                                           9.5   \n",
       "16                                          10.1   \n",
       "17                                          10.5   \n",
       "18                                          10.8   \n",
       "19                                          11.4   \n",
       "20                                          11.8   \n",
       "21                                          11.4   \n",
       "22                                          11.2   \n",
       "23                                          11.3   \n",
       "24                                          11.7   \n",
       "25                                          11.9   \n",
       "26                                          11.5   \n",
       "\n",
       "    Number of Persons Killed Per Ten Thousand Vehicles  \\\n",
       "0                                              103.50    \n",
       "1                                               53.10    \n",
       "2                                               28.20    \n",
       "3                                               23.30    \n",
       "4                                               23.40    \n",
       "5                                               22.10    \n",
       "6                                               20.60    \n",
       "7                                               19.30    \n",
       "8                                               18.30    \n",
       "9                                               16.20    \n",
       "10                                              14.70    \n",
       "11                                              14.40    \n",
       "12                                              12.80    \n",
       "13                                              12.70    \n",
       "14                                              11.70    \n",
       "15                                              11.80    \n",
       "16                                              11.80    \n",
       "17                                              11.40    \n",
       "18                                              10.90    \n",
       "19                                              10.50    \n",
       "20                                              10.00    \n",
       "21                                               8.70    \n",
       "22                                               7.60    \n",
       "23                                               7.30    \n",
       "24                                               7.00    \n",
       "25                                               6.60    \n",
       "26                                              12.75    \n",
       "\n",
       "    Number of Persons Killed per Ten Thousand Kms of Roads  \\\n",
       "0                                               122.0        \n",
       "1                                               160.9        \n",
       "2                                               272.7        \n",
       "3                                               223.0        \n",
       "4                                               237.9        \n",
       "5                                               233.1        \n",
       "6                                               233.3        \n",
       "7                                               247.6        \n",
       "8                                               248.6        \n",
       "9                                               238.0        \n",
       "10                                              239.8        \n",
       "11                                              247.1        \n",
       "12                                              243.7        \n",
       "13                                              255.7        \n",
       "14                                              249.3        \n",
       "15                                              272.5        \n",
       "16                                              284.9        \n",
       "17                                              291.7        \n",
       "18                                              281.0        \n",
       "19                                              293.5        \n",
       "20                                              304.7        \n",
       "21                                              284.2        \n",
       "22                                              262.9        \n",
       "23                                              258.5        \n",
       "24                                              267.0        \n",
       "25                                              269.1        \n",
       "26                                              252.5        \n",
       "\n",
       "    Number of Persons Injured per Lakh Population  \\\n",
       "0                                            13.0   \n",
       "1                                            16.2   \n",
       "2                                            29.2   \n",
       "3                                            34.5   \n",
       "4                                            35.0   \n",
       "5                                            39.2   \n",
       "6                                            39.4   \n",
       "7                                            39.9   \n",
       "8                                            37.7   \n",
       "9                                            39.3   \n",
       "10                                           39.4   \n",
       "11                                           39.1   \n",
       "12                                           41.0   \n",
       "13                                           43.0   \n",
       "14                                           42.5   \n",
       "15                                           44.6   \n",
       "16                                           45.5   \n",
       "17                                           45.7   \n",
       "18                                           44.4   \n",
       "19                                           44.8   \n",
       "20                                           42.3   \n",
       "21                                           42.2   \n",
       "22                                           40.4   \n",
       "23                                           39.8   \n",
       "24                                           39.9   \n",
       "25                                           39.0   \n",
       "26                                           36.7   \n",
       "\n",
       "    Number of Persons Injured Per Ten Thousand Vehicles  \\\n",
       "0                                               500.4     \n",
       "1                                               241.3     \n",
       "2                                               127.5     \n",
       "3                                               112.6     \n",
       "4                                               106.7     \n",
       "5                                               109.4     \n",
       "6                                               101.4     \n",
       "7                                                94.4     \n",
       "8                                                83.6     \n",
       "9                                                81.7     \n",
       "10                                               73.7     \n",
       "11                                               69.4     \n",
       "12                                               64.9     \n",
       "13                                               63.9     \n",
       "14                                               57.1     \n",
       "15                                               55.4     \n",
       "16                                               53.1     \n",
       "17                                               49.7     \n",
       "18                                               44.8     \n",
       "19                                               41.3     \n",
       "20                                               36.0     \n",
       "21                                               32.0     \n",
       "22                                               27.3     \n",
       "23                                               25.9     \n",
       "24                                               23.8     \n",
       "25                                               21.5     \n",
       "26                                               64.4     \n",
       "\n",
       "    Number of Persons Injured Per Ten Thousand Kms of Roads  \n",
       "0                                               589.7        \n",
       "1                                               731.3        \n",
       "2                                              1230.4        \n",
       "3                                              1077.5        \n",
       "4                                              1086.4        \n",
       "5                                              1153.8        \n",
       "6                                              1147.0        \n",
       "7                                              1210.1        \n",
       "8                                              1137.7        \n",
       "9                                              1204.0        \n",
       "10                                             1201.2        \n",
       "11                                             1192.8        \n",
       "12                                             1233.1        \n",
       "13                                             1282.7        \n",
       "14                                             1221.5        \n",
       "15                                             1279.4        \n",
       "16                                             1278.1        \n",
       "17                                             1273.1        \n",
       "18                                             1152.8        \n",
       "19                                             1151.2        \n",
       "20                                             1093.5        \n",
       "21                                             1047.5        \n",
       "22                                              945.9        \n",
       "23                                              913.4        \n",
       "24                                              914.2        \n",
       "25                                              882.7        \n",
       "26                                             1152.0        "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Total Number of Persons Killed (in numbers)',axis = 1)\n",
    "Y = data['Total Number of Persons Killed (in numbers)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=regressor.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2155.76, 0.46)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "mse=round((mean_squared_error(Y_test,Y_pred))/100, 2)\n",
    "rmse = round((sqrt(mse))/100 ,2)\n",
    "mse ,rmse\n",
    "\n",
    "\n",
    "## In order to reduce my MSE value I want to go for feature selection by Backward Elimination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Total Number of Persons Killed (in numbers)</td> <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                <td>OLS</td>                     <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                          <td>Least Squares</td>                <th>  F-statistic:       </th> <td>3.267e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                          <td>Sat, 12 Oct 2019</td>               <th>  Prob (F-statistic):</th> <td>2.40e-23</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                              <td>12:15:31</td>                   <th>  Log-Likelihood:    </th> <td> -177.53</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>                   <td>    27</td>                    <th>  AIC:               </th> <td>   387.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                       <td>    11</td>                    <th>  BIC:               </th> <td>   407.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                           <td>    15</td>                    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                   <td>nonrobust</td>                  <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                              <td></td>                                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                    <td> -1.51e+06</td> <td> 9.67e+05</td> <td>   -1.561</td> <td> 0.147</td> <td>-3.64e+06</td> <td> 6.19e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Years</th>                                                    <td>  752.5839</td> <td>  492.626</td> <td>    1.528</td> <td> 0.155</td> <td> -331.679</td> <td> 1836.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Road Accidents (in numbers)</th>              <td>   -0.0457</td> <td>    0.107</td> <td>   -0.429</td> <td> 0.676</td> <td>   -0.280</td> <td>    0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Persons Injured (in numbers)</th>             <td>    0.2104</td> <td>    0.099</td> <td>    2.122</td> <td> 0.057</td> <td>   -0.008</td> <td>    0.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Population of India (in thousands)</th>                       <td>    0.0254</td> <td>    0.032</td> <td>    0.803</td> <td> 0.439</td> <td>   -0.044</td> <td>    0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Registered Motor Vehicles (in thousands)</th> <td>    0.0113</td> <td>    0.018</td> <td>    0.623</td> <td> 0.546</td> <td>   -0.029</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Road Length (in kms)</th>                                     <td>   -0.0041</td> <td>    0.002</td> <td>   -1.846</td> <td> 0.092</td> <td>   -0.009</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Lakh Population</th>                  <td> -190.6816</td> <td> 1455.938</td> <td>   -0.131</td> <td> 0.898</td> <td>-3395.180</td> <td> 3013.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Ten Thousand Vehicles</th>            <td> -111.7085</td> <td>   51.978</td> <td>   -2.149</td> <td> 0.055</td> <td> -226.111</td> <td>    2.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Ten Thousand Kms of Roads</th>        <td>   22.6068</td> <td>   22.904</td> <td>    0.987</td> <td> 0.345</td> <td>  -27.805</td> <td>   73.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Lakh Population</th>             <td> 8499.3798</td> <td> 1710.853</td> <td>    4.968</td> <td> 0.000</td> <td> 4733.818</td> <td> 1.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Ten Thousand Vehicles</th>       <td>-1333.7168</td> <td>  395.030</td> <td>   -3.376</td> <td> 0.006</td> <td>-2203.172</td> <td> -464.261</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed per Ten Thousand Kms of Roads</th>   <td>  156.2267</td> <td>   58.035</td> <td>    2.692</td> <td> 0.021</td> <td>   28.493</td> <td>  283.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured per Lakh Population</th>            <td>-1151.8235</td> <td> 1510.752</td> <td>   -0.762</td> <td> 0.462</td> <td>-4476.965</td> <td> 2173.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured Per Ten Thousand Vehicles</th>      <td>  496.0578</td> <td>  122.646</td> <td>    4.045</td> <td> 0.002</td> <td>  226.116</td> <td>  765.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured Per Ten Thousand Kms of Roads</th>  <td>  -68.7774</td> <td>   27.947</td> <td>   -2.461</td> <td> 0.032</td> <td> -130.287</td> <td>   -7.268</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.486</td> <th>  Durbin-Watson:     </th> <td>   2.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.289</td> <th>  Jarque-Bera (JB):  </th> <td>   1.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.207</td> <th>  Prob(JB):          </th> <td>   0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.926</td> <th>  Cond. No.          </th> <td>7.52e+10</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 7.52e+10. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                         OLS Regression Results                                        \n",
       "=======================================================================================================\n",
       "Dep. Variable:     Total Number of Persons Killed (in numbers)   R-squared:                       1.000\n",
       "Model:                                                     OLS   Adj. R-squared:                  1.000\n",
       "Method:                                          Least Squares   F-statistic:                 3.267e+04\n",
       "Date:                                         Sat, 12 Oct 2019   Prob (F-statistic):           2.40e-23\n",
       "Time:                                                 12:15:31   Log-Likelihood:                -177.53\n",
       "No. Observations:                                           27   AIC:                             387.1\n",
       "Df Residuals:                                               11   BIC:                             407.8\n",
       "Df Model:                                                   15                                         \n",
       "Covariance Type:                                     nonrobust                                         \n",
       "============================================================================================================================\n",
       "                                                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                     -1.51e+06   9.67e+05     -1.561      0.147   -3.64e+06    6.19e+05\n",
       "Years                                                      752.5839    492.626      1.528      0.155    -331.679    1836.846\n",
       "Total Number of Road Accidents (in numbers)                 -0.0457      0.107     -0.429      0.676      -0.280       0.189\n",
       "Total Number of Persons Injured (in numbers)                 0.2104      0.099      2.122      0.057      -0.008       0.429\n",
       "Population of India (in thousands)                           0.0254      0.032      0.803      0.439      -0.044       0.095\n",
       "Total Number of Registered Motor Vehicles (in thousands)     0.0113      0.018      0.623      0.546      -0.029       0.051\n",
       "Road Length (in kms)                                        -0.0041      0.002     -1.846      0.092      -0.009       0.001\n",
       "Number of Accidents per Lakh Population                   -190.6816   1455.938     -0.131      0.898   -3395.180    3013.817\n",
       "Number of Accidents per Ten Thousand Vehicles             -111.7085     51.978     -2.149      0.055    -226.111       2.694\n",
       "Number of Accidents per Ten Thousand Kms of Roads           22.6068     22.904      0.987      0.345     -27.805      73.018\n",
       "Number of Persons Killed Per Lakh Population              8499.3798   1710.853      4.968      0.000    4733.818    1.23e+04\n",
       "Number of Persons Killed Per Ten Thousand Vehicles       -1333.7168    395.030     -3.376      0.006   -2203.172    -464.261\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads     156.2267     58.035      2.692      0.021      28.493     283.961\n",
       "Number of Persons Injured per Lakh Population            -1151.8235   1510.752     -0.762      0.462   -4476.965    2173.318\n",
       "Number of Persons Injured Per Ten Thousand Vehicles        496.0578    122.646      4.045      0.002     226.116     765.999\n",
       "Number of Persons Injured Per Ten Thousand Kms of Roads    -68.7774     27.947     -2.461      0.032    -130.287      -7.268\n",
       "==============================================================================\n",
       "Omnibus:                        2.486   Durbin-Watson:                   2.660\n",
       "Prob(Omnibus):                  0.289   Jarque-Bera (JB):                1.158\n",
       "Skew:                          -0.207   Prob(JB):                        0.560\n",
       "Kurtosis:                       3.926   Cond. No.                     7.52e+10\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.52e+10. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X=sm.add_constant(X)\n",
    "model=sm.OLS(Y,X).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Total Number of Persons Killed (in numbers)</td> <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                <td>OLS</td>                     <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                          <td>Least Squares</td>                <th>  F-statistic:       </th> <td>2.463e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                          <td>Sat, 12 Oct 2019</td>               <th>  Prob (F-statistic):</th> <td>2.53e-26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                              <td>12:30:19</td>                   <th>  Log-Likelihood:    </th> <td> -185.53</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>                   <td>    27</td>                    <th>  AIC:               </th> <td>   399.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                       <td>    13</td>                    <th>  BIC:               </th> <td>   417.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                           <td>    13</td>                    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                   <td>nonrobust</td>                  <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                              <td></td>                                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                    <td>  926.2423</td> <td> 2.71e+04</td> <td>    0.034</td> <td> 0.973</td> <td>-5.77e+04</td> <td> 5.96e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Road Accidents (in numbers)</th>              <td>   -0.0558</td> <td>    0.131</td> <td>   -0.425</td> <td> 0.678</td> <td>   -0.339</td> <td>    0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Persons Injured (in numbers)</th>             <td>    0.2597</td> <td>    0.121</td> <td>    2.153</td> <td> 0.051</td> <td>   -0.001</td> <td>    0.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Population of India (in thousands)</th>                       <td>    0.0128</td> <td>    0.025</td> <td>    0.512</td> <td> 0.617</td> <td>   -0.041</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Number of Registered Motor Vehicles (in thousands)</th> <td>    0.0137</td> <td>    0.019</td> <td>    0.737</td> <td> 0.474</td> <td>   -0.026</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Road Length (in kms)</th>                                     <td>   -0.0019</td> <td>    0.002</td> <td>   -0.833</td> <td> 0.420</td> <td>   -0.007</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Lakh Population</th>                  <td>  809.7224</td> <td> 1694.339</td> <td>    0.478</td> <td> 0.641</td> <td>-2850.675</td> <td> 4470.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Ten Thousand Vehicles</th>            <td>  -43.6413</td> <td>   55.056</td> <td>   -0.793</td> <td> 0.442</td> <td> -162.583</td> <td>   75.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Accidents per Ten Thousand Kms of Roads</th>        <td>  -16.0660</td> <td>   10.094</td> <td>   -1.592</td> <td> 0.135</td> <td>  -37.873</td> <td>    5.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Lakh Population</th>             <td> 1.181e+04</td> <td> 1387.762</td> <td>    8.511</td> <td> 0.000</td> <td> 8812.992</td> <td> 1.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Ten Thousand Vehicles</th>       <td>-1192.1713</td> <td>  480.101</td> <td>   -2.483</td> <td> 0.027</td> <td>-2229.366</td> <td> -154.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed per Ten Thousand Kms of Roads</th>   <td>   47.9955</td> <td>   45.267</td> <td>    1.060</td> <td> 0.308</td> <td>  -49.798</td> <td>  145.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured per Lakh Population</th>            <td>-3309.6853</td> <td> 1467.388</td> <td>   -2.255</td> <td> 0.042</td> <td>-6479.784</td> <td> -139.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured Per Ten Thousand Vehicles</th>      <td>  318.8232</td> <td>  129.480</td> <td>    2.462</td> <td> 0.029</td> <td>   39.099</td> <td>  598.547</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.390</td> <th>  Durbin-Watson:     </th> <td>   2.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.823</td> <th>  Jarque-Bera (JB):  </th> <td>   0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.172</td> <th>  Prob(JB):          </th> <td>   0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.938</td> <th>  Cond. No.          </th> <td>1.71e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.71e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                         OLS Regression Results                                        \n",
       "=======================================================================================================\n",
       "Dep. Variable:     Total Number of Persons Killed (in numbers)   R-squared:                       1.000\n",
       "Model:                                                     OLS   Adj. R-squared:                  1.000\n",
       "Method:                                          Least Squares   F-statistic:                 2.463e+04\n",
       "Date:                                         Sat, 12 Oct 2019   Prob (F-statistic):           2.53e-26\n",
       "Time:                                                 12:30:19   Log-Likelihood:                -185.53\n",
       "No. Observations:                                           27   AIC:                             399.1\n",
       "Df Residuals:                                               13   BIC:                             417.2\n",
       "Df Model:                                                   13                                         \n",
       "Covariance Type:                                     nonrobust                                         \n",
       "============================================================================================================================\n",
       "                                                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                      926.2423   2.71e+04      0.034      0.973   -5.77e+04    5.96e+04\n",
       "Total Number of Road Accidents (in numbers)                 -0.0558      0.131     -0.425      0.678      -0.339       0.228\n",
       "Total Number of Persons Injured (in numbers)                 0.2597      0.121      2.153      0.051      -0.001       0.520\n",
       "Population of India (in thousands)                           0.0128      0.025      0.512      0.617      -0.041       0.067\n",
       "Total Number of Registered Motor Vehicles (in thousands)     0.0137      0.019      0.737      0.474      -0.026       0.054\n",
       "Road Length (in kms)                                        -0.0019      0.002     -0.833      0.420      -0.007       0.003\n",
       "Number of Accidents per Lakh Population                    809.7224   1694.339      0.478      0.641   -2850.675    4470.119\n",
       "Number of Accidents per Ten Thousand Vehicles              -43.6413     55.056     -0.793      0.442    -162.583      75.301\n",
       "Number of Accidents per Ten Thousand Kms of Roads          -16.0660     10.094     -1.592      0.135     -37.873       5.741\n",
       "Number of Persons Killed Per Lakh Population              1.181e+04   1387.762      8.511      0.000    8812.992    1.48e+04\n",
       "Number of Persons Killed Per Ten Thousand Vehicles       -1192.1713    480.101     -2.483      0.027   -2229.366    -154.977\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads      47.9955     45.267      1.060      0.308     -49.798     145.789\n",
       "Number of Persons Injured per Lakh Population            -3309.6853   1467.388     -2.255      0.042   -6479.784    -139.586\n",
       "Number of Persons Injured Per Ten Thousand Vehicles        318.8232    129.480      2.462      0.029      39.099     598.547\n",
       "==============================================================================\n",
       "Omnibus:                        0.390   Durbin-Watson:                   2.946\n",
       "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.138\n",
       "Skew:                          -0.172   Prob(JB):                        0.933\n",
       "Kurtosis:                       2.938   Cond. No.                     1.71e+09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.71e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X.drop(['Years','Number of Persons Injured Per Ten Thousand Kms of Roads'],axis=1)\n",
    "model=sm.OLS(Y,X).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Total Number of Persons Killed (in numbers)</td> <th>  R-squared:         </th> <td>   0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                <td>OLS</td>                     <th>  Adj. R-squared:    </th> <td>   0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                          <td>Least Squares</td>                <th>  F-statistic:       </th> <td>   5708.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                          <td>Sat, 12 Oct 2019</td>               <th>  Prob (F-statistic):</th> <td>3.02e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                              <td>12:33:32</td>                   <th>  Log-Likelihood:    </th> <td> -221.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>                   <td>    27</td>                    <th>  AIC:               </th> <td>   457.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                       <td>    20</td>                    <th>  BIC:               </th> <td>   466.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                           <td>     6</td>                    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                   <td>nonrobust</td>                  <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                             <td></td>                               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                  <td> -1.14e+05</td> <td> 6964.502</td> <td>  -16.372</td> <td> 0.000</td> <td>-1.29e+05</td> <td>-9.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Population of India (in thousands)</th>                     <td>    0.1201</td> <td>    0.008</td> <td>   14.705</td> <td> 0.000</td> <td>    0.103</td> <td>    0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Lakh Population</th>           <td> 1.016e+04</td> <td>  623.727</td> <td>   16.287</td> <td> 0.000</td> <td> 8857.674</td> <td> 1.15e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed Per Ten Thousand Vehicles</th>     <td> 1760.0892</td> <td>  305.437</td> <td>    5.763</td> <td> 0.000</td> <td> 1122.960</td> <td> 2397.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Killed per Ten Thousand Kms of Roads</th> <td>  -18.6781</td> <td>   17.169</td> <td>   -1.088</td> <td> 0.290</td> <td>  -54.493</td> <td>   17.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured per Lakh Population</th>          <td> -242.8217</td> <td>   93.004</td> <td>   -2.611</td> <td> 0.017</td> <td> -436.825</td> <td>  -48.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Persons Injured Per Ten Thousand Vehicles</th>    <td> -279.5050</td> <td>   60.632</td> <td>   -4.610</td> <td> 0.000</td> <td> -405.981</td> <td> -153.029</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.313</td> <th>  Durbin-Watson:     </th> <td>   2.531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  13.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.293</td> <th>  Prob(JB):          </th> <td> 0.00128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.270</td> <th>  Cond. No.          </th> <td>3.78e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.78e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                         OLS Regression Results                                        \n",
       "=======================================================================================================\n",
       "Dep. Variable:     Total Number of Persons Killed (in numbers)   R-squared:                       0.999\n",
       "Model:                                                     OLS   Adj. R-squared:                  0.999\n",
       "Method:                                          Least Squares   F-statistic:                     5708.\n",
       "Date:                                         Sat, 12 Oct 2019   Prob (F-statistic):           3.02e-31\n",
       "Time:                                                 12:33:32   Log-Likelihood:                -221.52\n",
       "No. Observations:                                           27   AIC:                             457.0\n",
       "Df Residuals:                                               20   BIC:                             466.1\n",
       "Df Model:                                                    6                                         \n",
       "Covariance Type:                                     nonrobust                                         \n",
       "==========================================================================================================================\n",
       "                                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                   -1.14e+05   6964.502    -16.372      0.000   -1.29e+05   -9.95e+04\n",
       "Population of India (in thousands)                         0.1201      0.008     14.705      0.000       0.103       0.137\n",
       "Number of Persons Killed Per Lakh Population            1.016e+04    623.727     16.287      0.000    8857.674    1.15e+04\n",
       "Number of Persons Killed Per Ten Thousand Vehicles      1760.0892    305.437      5.763      0.000    1122.960    2397.219\n",
       "Number of Persons Killed per Ten Thousand Kms of Roads   -18.6781     17.169     -1.088      0.290     -54.493      17.137\n",
       "Number of Persons Injured per Lakh Population           -242.8217     93.004     -2.611      0.017    -436.825     -48.819\n",
       "Number of Persons Injured Per Ten Thousand Vehicles     -279.5050     60.632     -4.610      0.000    -405.981    -153.029\n",
       "==============================================================================\n",
       "Omnibus:                       13.313   Durbin-Watson:                   2.531\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               13.323\n",
       "Skew:                          -1.293   Prob(JB):                      0.00128\n",
       "Kurtosis:                       5.270   Cond. No.                     3.78e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.78e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X.drop(['Number of Accidents per Ten Thousand Kms of Roads','Number of Accidents per Ten Thousand Vehicles','Number of Accidents per Lakh Population','Road Length (in kms)','Total Number of Registered Motor Vehicles (in thousands)','Total Number of Persons Injured (in numbers)','Total Number of Road Accidents (in numbers)'],axis=1)\n",
    "model=sm.OLS(Y,X).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8456.15, 0.92)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=round((mean_squared_error(Y_test,Y_pred))/100, 2)\n",
    "rmse = round((sqrt(mse))/100 ,2)\n",
    "mse ,rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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