UnsupervisedLearningProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3839,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all required libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3840,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the data file\n",
    "vehicles=pd.read_csv('vehicle.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3841,
   "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>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6</td>\n",
       "      <td>255.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>169</td>\n",
       "      <td>280.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>6</td>\n",
       "      <td>153.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>176.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>204</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>90</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>9</td>\n",
       "      <td>137.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>146</td>\n",
       "      <td>162.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>202</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>34.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7</td>\n",
       "      <td>122.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>127</td>\n",
       "      <td>141.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>208</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>93</td>\n",
       "      <td>44.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>11</td>\n",
       "      <td>183.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>146</td>\n",
       "      <td>202.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>204</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95         48.0                  83.0         178.0   \n",
       "1           91         41.0                  84.0         141.0   \n",
       "2          104         50.0                 106.0         209.0   \n",
       "3           93         41.0                  82.0         159.0   \n",
       "4           85         44.0                  70.0         205.0   \n",
       "5          107          NaN                 106.0         172.0   \n",
       "6           97         43.0                  73.0         173.0   \n",
       "7           90         43.0                  66.0         157.0   \n",
       "8           86         34.0                  62.0         140.0   \n",
       "9           93         44.0                  98.0           NaN   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                  72.0                       10          162.0   \n",
       "1                  57.0                        9          149.0   \n",
       "2                  66.0                       10          207.0   \n",
       "3                  63.0                        9          144.0   \n",
       "4                 103.0                       52          149.0   \n",
       "5                  50.0                        6          255.0   \n",
       "6                  65.0                        6          153.0   \n",
       "7                  65.0                        9          137.0   \n",
       "8                  61.0                        7          122.0   \n",
       "9                  62.0                       11          183.0   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0           42.0                    20.0                        159   \n",
       "1           45.0                    19.0                        143   \n",
       "2           32.0                    23.0                        158   \n",
       "3           46.0                    19.0                        143   \n",
       "4           45.0                    19.0                        144   \n",
       "5           26.0                    28.0                        169   \n",
       "6           42.0                    19.0                        143   \n",
       "7           48.0                    18.0                        146   \n",
       "8           54.0                    17.0                        127   \n",
       "9           36.0                    22.0                        146   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0            176.0              379.0                      184.0   \n",
       "1            170.0              330.0                      158.0   \n",
       "2            223.0              635.0                      220.0   \n",
       "3            160.0              309.0                      127.0   \n",
       "4            241.0              325.0                      188.0   \n",
       "5            280.0              957.0                      264.0   \n",
       "6            176.0              361.0                      172.0   \n",
       "7            162.0              281.0                      164.0   \n",
       "8            141.0              223.0                      112.0   \n",
       "9            202.0              505.0                      152.0   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                         70.0             6.0              16.0   \n",
       "1                         72.0             9.0              14.0   \n",
       "2                         73.0            14.0               9.0   \n",
       "3                         63.0             6.0              10.0   \n",
       "4                        127.0             9.0              11.0   \n",
       "5                         85.0             5.0               9.0   \n",
       "6                         66.0            13.0               1.0   \n",
       "7                         67.0             3.0               3.0   \n",
       "8                         64.0             2.0              14.0   \n",
       "9                         64.0             4.0              14.0   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  \n",
       "0             187.0            197   van  \n",
       "1             189.0            199   van  \n",
       "2             188.0            196   car  \n",
       "3             199.0            207   van  \n",
       "4             180.0            183   bus  \n",
       "5             181.0            183   bus  \n",
       "6             200.0            204   bus  \n",
       "7             193.0            202   van  \n",
       "8             200.0            208   van  \n",
       "9             195.0            204   car  "
      ]
     },
     "execution_count": 3841,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.head(10)\n",
    "#From below data, we can see that most of the values are numeric. We do have NAN values present in the data set\n",
    "#Let's analyze it further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3842,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data pre-processing – Perform all the necessary pre-processing on the data ready to be fed to an Unsupervised algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3843,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 846 entries, 0 to 845\n",
      "Data columns (total 19 columns):\n",
      "compactness                    846 non-null int64\n",
      "circularity                    841 non-null float64\n",
      "distance_circularity           842 non-null float64\n",
      "radius_ratio                   840 non-null float64\n",
      "pr.axis_aspect_ratio           844 non-null float64\n",
      "max.length_aspect_ratio        846 non-null int64\n",
      "scatter_ratio                  845 non-null float64\n",
      "elongatedness                  845 non-null float64\n",
      "pr.axis_rectangularity         843 non-null float64\n",
      "max.length_rectangularity      846 non-null int64\n",
      "scaled_variance                843 non-null float64\n",
      "scaled_variance.1              844 non-null float64\n",
      "scaled_radius_of_gyration      844 non-null float64\n",
      "scaled_radius_of_gyration.1    842 non-null float64\n",
      "skewness_about                 840 non-null float64\n",
      "skewness_about.1               845 non-null float64\n",
      "skewness_about.2               845 non-null float64\n",
      "hollows_ratio                  846 non-null int64\n",
      "class                          846 non-null object\n",
      "dtypes: float64(14), int64(4), object(1)\n",
      "memory usage: 125.7+ KB\n"
     ]
    }
   ],
   "source": [
    "vehicles.info()\n",
    "#The data set has 846 rows and 19 columns, all are numeric except column 'class' which is of type object. \n",
    "#This indicates that there is categorical variable which needs to represented in numeric format.\n",
    "#From below data it is evident that not all 846 rows has values, some cells are empty "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3844,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>compactness</td>\n",
       "      <td>846.0</td>\n",
       "      <td>93.678487</td>\n",
       "      <td>8.234474</td>\n",
       "      <td>73.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>93.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>circularity</td>\n",
       "      <td>841.0</td>\n",
       "      <td>44.828775</td>\n",
       "      <td>6.152172</td>\n",
       "      <td>33.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>44.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>distance_circularity</td>\n",
       "      <td>842.0</td>\n",
       "      <td>82.110451</td>\n",
       "      <td>15.778292</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_ratio</td>\n",
       "      <td>840.0</td>\n",
       "      <td>168.888095</td>\n",
       "      <td>33.520198</td>\n",
       "      <td>104.0</td>\n",
       "      <td>141.00</td>\n",
       "      <td>167.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pr.axis_aspect_ratio</td>\n",
       "      <td>844.0</td>\n",
       "      <td>61.678910</td>\n",
       "      <td>7.891463</td>\n",
       "      <td>47.0</td>\n",
       "      <td>57.00</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max.length_aspect_ratio</td>\n",
       "      <td>846.0</td>\n",
       "      <td>8.567376</td>\n",
       "      <td>4.601217</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scatter_ratio</td>\n",
       "      <td>845.0</td>\n",
       "      <td>168.901775</td>\n",
       "      <td>33.214848</td>\n",
       "      <td>112.0</td>\n",
       "      <td>147.00</td>\n",
       "      <td>157.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>elongatedness</td>\n",
       "      <td>845.0</td>\n",
       "      <td>40.933728</td>\n",
       "      <td>7.816186</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pr.axis_rectangularity</td>\n",
       "      <td>843.0</td>\n",
       "      <td>20.582444</td>\n",
       "      <td>2.592933</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max.length_rectangularity</td>\n",
       "      <td>846.0</td>\n",
       "      <td>147.998818</td>\n",
       "      <td>14.515652</td>\n",
       "      <td>118.0</td>\n",
       "      <td>137.00</td>\n",
       "      <td>146.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_variance</td>\n",
       "      <td>843.0</td>\n",
       "      <td>188.631079</td>\n",
       "      <td>31.411004</td>\n",
       "      <td>130.0</td>\n",
       "      <td>167.00</td>\n",
       "      <td>179.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_variance.1</td>\n",
       "      <td>844.0</td>\n",
       "      <td>439.494076</td>\n",
       "      <td>176.666903</td>\n",
       "      <td>184.0</td>\n",
       "      <td>318.00</td>\n",
       "      <td>363.5</td>\n",
       "      <td>587.0</td>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_radius_of_gyration</td>\n",
       "      <td>844.0</td>\n",
       "      <td>174.709716</td>\n",
       "      <td>32.584808</td>\n",
       "      <td>109.0</td>\n",
       "      <td>149.00</td>\n",
       "      <td>173.5</td>\n",
       "      <td>198.0</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_radius_of_gyration.1</td>\n",
       "      <td>842.0</td>\n",
       "      <td>72.447743</td>\n",
       "      <td>7.486190</td>\n",
       "      <td>59.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>71.5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about</td>\n",
       "      <td>840.0</td>\n",
       "      <td>6.364286</td>\n",
       "      <td>4.920649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about.1</td>\n",
       "      <td>845.0</td>\n",
       "      <td>12.602367</td>\n",
       "      <td>8.936081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about.2</td>\n",
       "      <td>845.0</td>\n",
       "      <td>188.919527</td>\n",
       "      <td>6.155809</td>\n",
       "      <td>176.0</td>\n",
       "      <td>184.00</td>\n",
       "      <td>188.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hollows_ratio</td>\n",
       "      <td>846.0</td>\n",
       "      <td>195.632388</td>\n",
       "      <td>7.438797</td>\n",
       "      <td>181.0</td>\n",
       "      <td>190.25</td>\n",
       "      <td>197.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count        mean         std    min     25%  \\\n",
       "compactness                  846.0   93.678487    8.234474   73.0   87.00   \n",
       "circularity                  841.0   44.828775    6.152172   33.0   40.00   \n",
       "distance_circularity         842.0   82.110451   15.778292   40.0   70.00   \n",
       "radius_ratio                 840.0  168.888095   33.520198  104.0  141.00   \n",
       "pr.axis_aspect_ratio         844.0   61.678910    7.891463   47.0   57.00   \n",
       "max.length_aspect_ratio      846.0    8.567376    4.601217    2.0    7.00   \n",
       "scatter_ratio                845.0  168.901775   33.214848  112.0  147.00   \n",
       "elongatedness                845.0   40.933728    7.816186   26.0   33.00   \n",
       "pr.axis_rectangularity       843.0   20.582444    2.592933   17.0   19.00   \n",
       "max.length_rectangularity    846.0  147.998818   14.515652  118.0  137.00   \n",
       "scaled_variance              843.0  188.631079   31.411004  130.0  167.00   \n",
       "scaled_variance.1            844.0  439.494076  176.666903  184.0  318.00   \n",
       "scaled_radius_of_gyration    844.0  174.709716   32.584808  109.0  149.00   \n",
       "scaled_radius_of_gyration.1  842.0   72.447743    7.486190   59.0   67.00   \n",
       "skewness_about               840.0    6.364286    4.920649    0.0    2.00   \n",
       "skewness_about.1             845.0   12.602367    8.936081    0.0    5.00   \n",
       "skewness_about.2             845.0  188.919527    6.155809  176.0  184.00   \n",
       "hollows_ratio                846.0  195.632388    7.438797  181.0  190.25   \n",
       "\n",
       "                               50%    75%     max  \n",
       "compactness                   93.0  100.0   119.0  \n",
       "circularity                   44.0   49.0    59.0  \n",
       "distance_circularity          80.0   98.0   112.0  \n",
       "radius_ratio                 167.0  195.0   333.0  \n",
       "pr.axis_aspect_ratio          61.0   65.0   138.0  \n",
       "max.length_aspect_ratio        8.0   10.0    55.0  \n",
       "scatter_ratio                157.0  198.0   265.0  \n",
       "elongatedness                 43.0   46.0    61.0  \n",
       "pr.axis_rectangularity        20.0   23.0    29.0  \n",
       "max.length_rectangularity    146.0  159.0   188.0  \n",
       "scaled_variance              179.0  217.0   320.0  \n",
       "scaled_variance.1            363.5  587.0  1018.0  \n",
       "scaled_radius_of_gyration    173.5  198.0   268.0  \n",
       "scaled_radius_of_gyration.1   71.5   75.0   135.0  \n",
       "skewness_about                 6.0    9.0    22.0  \n",
       "skewness_about.1              11.0   19.0    41.0  \n",
       "skewness_about.2             188.0  193.0   206.0  \n",
       "hollows_ratio                197.0  201.0   211.0  "
      ]
     },
     "execution_count": 3844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.describe().transpose()\n",
    "#Inference: From below data we can conclude that no negetive values are present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3845,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, compactness                    0\n",
       " circularity                    5\n",
       " distance_circularity           4\n",
       " radius_ratio                   6\n",
       " pr.axis_aspect_ratio           2\n",
       " max.length_aspect_ratio        0\n",
       " scatter_ratio                  1\n",
       " elongatedness                  1\n",
       " pr.axis_rectangularity         3\n",
       " max.length_rectangularity      0\n",
       " scaled_variance                3\n",
       " scaled_variance.1              2\n",
       " scaled_radius_of_gyration      2\n",
       " scaled_radius_of_gyration.1    4\n",
       " skewness_about                 6\n",
       " skewness_about.1               1\n",
       " skewness_about.2               1\n",
       " hollows_ratio                  0\n",
       " class                          0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 3845,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the presence of missing values.\n",
    "vehicles.isnull().values.any(),vehicles.isna().values.any(),vehicles.isna().sum()\n",
    "# Below data indicates that there are null and NAN values. \n",
    "#Inference:null values are present under columns-circularity,distance_circularity,radius_ratio,ps_axis_aspect_ratio,\n",
    "#scatter_ratio,elongatedness,pr.axis_rectangularity,scaled_variance,scaled_variance.1,scaled_radius_of_gyration,\n",
    "#scaled_radius_of_gyration_1,skewness_about,skewness_about.1,skewness_about.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3846,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9da626d0>"
      ]
     },
     "execution_count": 3846,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's reconfirm the understanding with heatmap\n",
    "sns.heatmap(vehicles.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "#From below graph, it is evident that values are missing (indicated by yellow bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3847,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking variables one bye one and proceeding with required data treatments in below steps.\n",
    "# Also we will perform univariate/bivariate (independent variable/dependent variable) analysis to understand the \n",
    "#each attributes and choose carefully which all attributes have to be a part of the analysis and why"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3848,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9eb68ad0>"
      ]
     },
     "execution_count": 3848,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyzing the variable-compactness\n",
    "sns.distplot(vehicles['compactness'], rug=True)\n",
    "#Inference: Variable 'compactness' has slight right skewed distribution in the given data set. \n",
    "#No outliers are found and data is concentrated between 75-112"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3849,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f008e10>"
      ]
     },
     "execution_count": 3849,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['compactness'])\n",
    "#No outliers, null/NAN values present w.r.t compactness and it looks clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3850,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f0b1d90>"
      ]
     },
     "execution_count": 3850,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-compactness vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['compactness'])\n",
    "#Inference: From below graph, it is evident that comapctness varies for each class. \n",
    "#Also van and bus has slightly right skewed distribution w.r.t compactness. Car has wide spread w.r.t compactness\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3851,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing the variable-circularity\n",
    "#From the previous analysis, we could see the presence of null/NAN values in circularity\n",
    "#Let's replace null values and NAN values, else we will not be able to visualize this numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3852,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48., 41., 50., 44., nan, 43., 34., 36., 46., 42., 49., 55., 54.,\n",
       "       56., 47., 37., 39., 53., 45., 38., 35., 40., 59., 52., 51., 58.,\n",
       "       57., 33.])"
      ]
     },
     "execution_count": 3852,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['circularity'].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'circularity' variable has any outliers as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3853,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f1aa150>"
      ]
     },
     "execution_count": 3853,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['circularity'])\n",
    "#Boxplot does not show the presence of outliers for circularity\n",
    "#So we can replace 'nan' with median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3854,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles['circularity'].fillna((vehicles['circularity'].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3855,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48., 41., 50., 44., 43., 34., 36., 46., 42., 49., 55., 54., 56.,\n",
       "       47., 37., 39., 53., 45., 38., 35., 40., 59., 52., 51., 58., 57.,\n",
       "       33.])"
      ]
     },
     "execution_count": 3855,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['circularity'].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3856,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f292950>"
      ]
     },
     "execution_count": 3856,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['circularity'], rug=True)\n",
    "#Inference: Variable circularity is somewhat symmetrically distributed \n",
    "#We could also see multiple modes for this variable across data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3857,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f4172d0>"
      ]
     },
     "execution_count": 3857,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-circularity vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['circularity'])\n",
    "#Inference: From below graph, it is evident that circularity varies for each class. \n",
    "#Also car has wide range of data spread w.r.t circularity and outliers are observed for bus w.r.t this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3858,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing the variable-distance_circularity\n",
    "#From the previous analysis, we could see the presence of null/NAN values in distance_circularity\n",
    "#Let's replace null values and NAN values, else we will not be able to visualize this numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3859,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 83.,  84., 106.,  82.,  70.,  73.,  66.,  62.,  98.,  74.,  85.,\n",
       "        79., 103.,  51.,  77., 100.,  75.,  53.,  64., 105.,  80.,  54.,\n",
       "        63., 107.,  nan,  72.,  86.,  68., 104.,  87.,  76.,  81.,  71.,\n",
       "       101.,  96.,  78., 108.,  91.,  89.,  94.,  92.,  60.,  57.,  65.,\n",
       "        50.,  88., 109.,  95.,  90.,  58.,  69.,  47.,  40.,  59., 110.,\n",
       "        93., 102., 112.,  61.,  42.,  49.,  44.,  52.,  55.])"
      ]
     },
     "execution_count": 3859,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['distance_circularity'].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'circularity' variable has any outliers as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9ea3fc50>"
      ]
     },
     "execution_count": 3860,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['distance_circularity'])\n",
    "#Inference: No outliers present, so we can directly replace Nan values with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3861,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles['distance_circularity'].fillna((vehicles['distance_circularity'].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3862,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 83.,  84., 106.,  82.,  70.,  73.,  66.,  62.,  98.,  74.,  85.,\n",
       "        79., 103.,  51.,  77., 100.,  75.,  53.,  64., 105.,  80.,  54.,\n",
       "        63., 107.,  72.,  86.,  68., 104.,  87.,  76.,  81.,  71., 101.,\n",
       "        96.,  78., 108.,  91.,  89.,  94.,  92.,  60.,  57.,  65.,  50.,\n",
       "        88., 109.,  95.,  90.,  58.,  69.,  47.,  40.,  59., 110.,  93.,\n",
       "       102., 112.,  61.,  42.,  49.,  44.,  52.,  55.])"
      ]
     },
     "execution_count": 3862,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['distance_circularity'].unique()\n",
    "#From below data, it is evident that nan values are replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3863,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f601b10>"
      ]
     },
     "execution_count": 3863,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['distance_circularity'],rug=True)\n",
    "#Inference: Data has multiple modes and slightly left tailed but no outliers present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3864,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f76a790>"
      ]
     },
     "execution_count": 3864,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-distance_circularity vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['distance_circularity'])\n",
    "#Inference: From below graph, it is evident that distance_circularity varies for each class. \n",
    "#Also car has higher and wide range of data spread w.r.t distance_circularity \n",
    "#and outliers are observed for bus w.r.t this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3865,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-'radius_ratio'\n",
    "#From previous analysis, we could see presence of nan values.Lets reconfirm and treat this in below steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3866,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([178., 141., 209., 159., 205., 172., 173., 157., 140.,  nan, 143.,\n",
       "       136., 171., 144., 203., 201., 109., 197., 186., 215., 153., 121.,\n",
       "       148., 219., 154., 119., 193., 129., 160., 151., 222., 177., 118.,\n",
       "       306., 176., 169., 214., 105., 137., 183., 220., 145., 133., 122.,\n",
       "       147., 115., 174., 228., 175., 185., 195., 221., 212., 135., 120.,\n",
       "       156., 125., 164., 161., 227., 191., 111., 170., 113., 127., 188.,\n",
       "       180., 116., 158., 162., 211., 152., 124., 252., 150., 130., 198.,\n",
       "       202., 199., 128., 142., 163., 155., 184., 165., 322., 194., 218.,\n",
       "       216., 223., 149., 131., 139., 179., 166., 187., 167., 231., 168.,\n",
       "       126., 206., 210., 110., 189., 134., 132., 230., 196., 208., 138.,\n",
       "       200., 225., 246., 207., 192., 117., 123., 146., 190., 182., 204.,\n",
       "       224., 333., 213., 226., 238., 181., 114., 104., 112., 234., 235.,\n",
       "       250., 232., 217.])"
      ]
     },
     "execution_count": 3866,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['radius_ratio'].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'radius_ratio' variable has any outliers as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3867,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f8723d0>"
      ]
     },
     "execution_count": 3867,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['radius_ratio'])\n",
    "#Boxplot shows the presence of outliers for radius_ratio (values>250 can be considered as outliers in below graph)\n",
    "#So we should replace outlier values with median first and then replace nan with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3868,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>compactness</td>\n",
       "      <td>4.0</td>\n",
       "      <td>88.75</td>\n",
       "      <td>4.991660</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.25</td>\n",
       "      <td>89.5</td>\n",
       "      <td>91.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>circularity</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.75</td>\n",
       "      <td>1.258306</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.25</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>distance_circularity</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>9.416298</td>\n",
       "      <td>66.0</td>\n",
       "      <td>78.75</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.25</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_ratio</td>\n",
       "      <td>4.0</td>\n",
       "      <td>303.25</td>\n",
       "      <td>35.920050</td>\n",
       "      <td>252.0</td>\n",
       "      <td>292.50</td>\n",
       "      <td>314.0</td>\n",
       "      <td>324.75</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pr.axis_aspect_ratio</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.75</td>\n",
       "      <td>5.852350</td>\n",
       "      <td>126.0</td>\n",
       "      <td>126.00</td>\n",
       "      <td>129.5</td>\n",
       "      <td>134.25</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max.length_aspect_ratio</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.50</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.75</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.75</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scatter_ratio</td>\n",
       "      <td>4.0</td>\n",
       "      <td>153.50</td>\n",
       "      <td>4.203173</td>\n",
       "      <td>148.0</td>\n",
       "      <td>151.75</td>\n",
       "      <td>154.0</td>\n",
       "      <td>155.75</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>elongatedness</td>\n",
       "      <td>4.0</td>\n",
       "      <td>43.75</td>\n",
       "      <td>0.957427</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>43.5</td>\n",
       "      <td>44.25</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pr.axis_rectangularity</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.25</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.25</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max.length_rectangularity</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154.50</td>\n",
       "      <td>7.852813</td>\n",
       "      <td>144.0</td>\n",
       "      <td>152.25</td>\n",
       "      <td>155.5</td>\n",
       "      <td>157.75</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_variance</td>\n",
       "      <td>4.0</td>\n",
       "      <td>264.50</td>\n",
       "      <td>41.444742</td>\n",
       "      <td>229.0</td>\n",
       "      <td>235.00</td>\n",
       "      <td>254.5</td>\n",
       "      <td>284.00</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_variance.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>347.50</td>\n",
       "      <td>16.114176</td>\n",
       "      <td>326.0</td>\n",
       "      <td>341.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>356.50</td>\n",
       "      <td>364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_radius_of_gyration</td>\n",
       "      <td>4.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>9.899495</td>\n",
       "      <td>176.0</td>\n",
       "      <td>182.75</td>\n",
       "      <td>186.0</td>\n",
       "      <td>190.25</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scaled_radius_of_gyration.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>117.25</td>\n",
       "      <td>15.585784</td>\n",
       "      <td>97.0</td>\n",
       "      <td>112.75</td>\n",
       "      <td>118.5</td>\n",
       "      <td>123.00</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>5.852350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.75</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>6.396614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>14.25</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>skewness_about.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>184.50</td>\n",
       "      <td>2.886751</td>\n",
       "      <td>181.0</td>\n",
       "      <td>183.25</td>\n",
       "      <td>184.5</td>\n",
       "      <td>185.75</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hollows_ratio</td>\n",
       "      <td>4.0</td>\n",
       "      <td>192.25</td>\n",
       "      <td>4.924429</td>\n",
       "      <td>185.0</td>\n",
       "      <td>191.75</td>\n",
       "      <td>194.0</td>\n",
       "      <td>194.50</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count    mean        std    min     25%    50%  \\\n",
       "compactness                    4.0   88.75   4.991660   82.0   87.25   89.5   \n",
       "circularity                    4.0   46.75   1.258306   45.0   46.50   47.0   \n",
       "distance_circularity           4.0   80.00   9.416298   66.0   78.75   84.0   \n",
       "radius_ratio                   4.0  303.25  35.920050  252.0  292.50  314.0   \n",
       "pr.axis_aspect_ratio           4.0  130.75   5.852350  126.0  126.00  129.5   \n",
       "max.length_aspect_ratio        4.0   49.50   1.732051   48.0   48.75   49.0   \n",
       "scatter_ratio                  4.0  153.50   4.203173  148.0  151.75  154.0   \n",
       "elongatedness                  4.0   43.75   0.957427   43.0   43.00   43.5   \n",
       "pr.axis_rectangularity         4.0   19.25   0.500000   19.0   19.00   19.0   \n",
       "max.length_rectangularity      4.0  154.50   7.852813  144.0  152.25  155.5   \n",
       "scaled_variance                4.0  264.50  41.444742  229.0  235.00  254.5   \n",
       "scaled_variance.1              4.0  347.50  16.114176  326.0  341.00  350.0   \n",
       "scaled_radius_of_gyration      4.0  187.00   9.899495  176.0  182.75  186.0   \n",
       "scaled_radius_of_gyration.1    4.0  117.25  15.585784   97.0  112.75  118.5   \n",
       "skewness_about                 4.0    3.25   5.852350    0.0    0.00    0.5   \n",
       "skewness_about.1               4.0    9.75   6.396614    1.0    7.00   11.5   \n",
       "skewness_about.2               4.0  184.50   2.886751  181.0  183.25  184.5   \n",
       "hollows_ratio                  4.0  192.25   4.924429  185.0  191.75  194.0   \n",
       "\n",
       "                                75%    max  \n",
       "compactness                   91.00   94.0  \n",
       "circularity                   47.25   48.0  \n",
       "distance_circularity          85.25   86.0  \n",
       "radius_ratio                 324.75  333.0  \n",
       "pr.axis_aspect_ratio         134.25  138.0  \n",
       "max.length_aspect_ratio       49.75   52.0  \n",
       "scatter_ratio                155.75  158.0  \n",
       "elongatedness                 44.25   45.0  \n",
       "pr.axis_rectangularity        19.25   20.0  \n",
       "max.length_rectangularity    157.75  163.0  \n",
       "scaled_variance              284.00  320.0  \n",
       "scaled_variance.1            356.50  364.0  \n",
       "scaled_radius_of_gyration    190.25  200.0  \n",
       "scaled_radius_of_gyration.1  123.00  135.0  \n",
       "skewness_about                 3.75   12.0  \n",
       "skewness_about.1              14.25   15.0  \n",
       "skewness_about.2             185.75  188.0  \n",
       "hollows_ratio                194.50  196.0  "
      ]
     },
     "execution_count": 3868,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[vehicles['radius_ratio']>250]. describe().transpose()\n",
    "#Inference: There are just 4 records which has outliers for radius_ratio variable. Lets replace it with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3869,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167.0"
      ]
     },
     "execution_count": 3869,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.radius_ratio.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3870,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing outliers with median value\n",
    "vehicles[\"radius_ratio\"]=np.where(vehicles[\"radius_ratio\"]>250, 167.0,vehicles[\"radius_ratio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3871,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"radius_ratio\"].fillna((vehicles[\"radius_ratio\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3872,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([178., 141., 209., 159., 205., 172., 173., 157., 140., 167., 143.,\n",
       "       136., 171., 144., 203., 201., 109., 197., 186., 215., 153., 121.,\n",
       "       148., 219., 154., 119., 193., 129., 160., 151., 222., 177., 118.,\n",
       "       176., 169., 214., 105., 137., 183., 220., 145., 133., 122., 147.,\n",
       "       115., 174., 228., 175., 185., 195., 221., 212., 135., 120., 156.,\n",
       "       125., 164., 161., 227., 191., 111., 170., 113., 127., 188., 180.,\n",
       "       116., 158., 162., 211., 152., 124., 150., 130., 198., 202., 199.,\n",
       "       128., 142., 163., 155., 184., 165., 194., 218., 216., 223., 149.,\n",
       "       131., 139., 179., 166., 187., 231., 168., 126., 206., 210., 110.,\n",
       "       189., 134., 132., 230., 196., 208., 138., 200., 225., 246., 207.,\n",
       "       192., 117., 123., 146., 190., 182., 204., 224., 213., 226., 238.,\n",
       "       181., 114., 104., 112., 234., 235., 250., 232., 217.])"
      ]
     },
     "execution_count": 3872,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"radius_ratio\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3873,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9f95b550>"
      ]
     },
     "execution_count": 3873,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['radius_ratio'],rug=True)\n",
    "#Inference: From the graph below, we can conclude that radius_ratio is symmetrically distributed \n",
    "#across the data set and concentration is found between values: 112-240"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3874,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9facde10>"
      ]
     },
     "execution_count": 3874,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-radius_ratio vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['radius_ratio'])\n",
    "#Inference: From below graph, it is evident that radius_ratio is on lower range for each class. \n",
    "#Also car has slightly higher range w.r.t radius_ratio\n",
    "#and outliers are observed for van w.r.t this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3875,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Moving forward with variable-'pr.axis_aspect_ratio'\n",
    "#This has nan values and hence needs to be treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3876,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 72.,  57.,  66.,  63., 103.,  50.,  65.,  61.,  62.,  55.,  68.,\n",
       "        58.,  71.,  52.,  69.,  nan,  64.,  59.,  67., 126.,  54.,  73.,\n",
       "        51.,  70.,  53.,  56.,  74.,  60.,  76.,  75.,  49., 133.,  47.,\n",
       "       102., 138.,  48.,  97., 105.])"
      ]
     },
     "execution_count": 3876,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"pr.axis_aspect_ratio\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3877,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9fbb8210>"
      ]
     },
     "execution_count": 3877,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEHCAYAAAB/UveQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAPhElEQVR4nO3df5Dc9V3H8ec7dykkbYFyoRGv2KNcFKUVZGILqB1UmCaiIGOZVjsmjMww6EyIaKdayTCjzYxUdBgIUqUVk7QVa7FFRkhGoIwRGMCkJCQIbVe5FiI/wtHSkiDNJR//+H6XO469JJfb2/dx+3zM7Ox3v/v9fj6f/Xy/97rvfnf3841SCpKkHHOyGyBJ3cwQlqREhrAkJTKEJSmRISxJiXons/CCBQvKwMDANDVFkmanLVu2vFBKOa7Vc5MK4YGBATZv3tyeVklSl4iIb0/0nKcjJCmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKNKlrzL2ZrVmzhkajMen1du7cCUB/f/+k1hscHGTFihWTrk9Sd+maEG40Gmzd8Tj75h87qfV69rwEwLOvHnpX9ex5cVJ1SOpeXRPCAPvmH8srJ//KpNaZ98SdAJNar7mOJB2M54QlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKSlMgQlqREhrAkJTKEJSlRR0J4zZo1rFmzphNVaQJuA2lm6u1EJY1GoxPV6ADcBtLM5OkISUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhNXVhoeHufzyyxkeHm5LGQcqrx11zUaNRoPzzjuPRqMxLeVPtd+ne7sZwupq69atY/v27axfv74tZRyovHbUNRutXr2a3bt3s3r16mkpf6r9Pt3bzRBW1xoeHmbjxo2UUti4ceNhHemMLWPDhg1s2LChZXntqGs2ajQaDA0NATA0NNT2o+Gp9nsntltv20tsYefOnbzyyiusXLmyE9W11Gg0mPPD0pG65vzf92k0fpD6esdrNBrMmzcvuxkzyrp169i/fz8A+/btY/369VxxxRWHXcbevXtfmz++vHbUNRuNP/pdvXo1a9eubVv5U+33Tmy3gx4JR8SlEbE5Ijbv2rWrrZVLme6++25GRkYAGBkZ4a677ppSGaUUSikty2tHXbNR8yh4osdTNdV+78R2O+iRcCnlJuAmgMWLFx/WoWR/fz8A11133eGs3hYrV65ky/8815G69h95FIPvWZj6esebSUflM8U555zDnXfeycjICL29vZx77rlTKiMigCqMx5fXjrpmo4GBgdcF78DAQFvLn2q/d2K7eU5YXWv58uXMmVP9CfT09LBs2bIplTF37lx6e3tblteOumajVatWHfDxVE213zux3Qxhda2+vj6WLFlCRLBkyRL6+vqmVMbSpUtZunRpy/LaUddsNDg4+NrR78DAAIODg20tf6r93ont1pEP5qSZavny5QwNDU3pCGd8GROV1466ZqNVq1axcuXKth8FN02136d7uxnC6mp9fX1cf/31bS1jovLaUddsNDg4yB133DFt5U+136d7u3k6QpISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJejtRyeDgYCeq0QG4DaSZqSMhvGLFik5UowNwG0gzk6cjJCmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCUyhCUpkSEsSYkMYUlKZAhLUiJDWJISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRIawJCXqzW5AJ/XseZF5T9w5yXWGASa1Xs+eF4GFk6pHUnfqmhAeHBw8rPV27hwBoL9/MqG68LDrk9RduiaEV6xYkd0ESXoDzwlLUiJDWJISGcKSlMgQlqREhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUyBCWpESGsCQlMoQlKZEhLEmJDGFJSmQIS1IiQ1iSEhnCkpTIEJakRFFKOfSFI3YB356+5swYC4AXshsxQ9gXo+yLUfbFqEPpi3eXUo5r9cSkQrhbRMTmUsri7HbMBPbFKPtilH0xaqp94ekISUpkCEtSIkO4tZuyGzCD2Bej7ItR9sWoKfWF54QlKZFHwpKUyBCWpESGMBARx0TErRHxREQ8HhFnRsSxEXFXRHyrvn9Hdjs7ISKuiIjHImJHRNwSEUdGxIkR8VDdF1+KiLdkt3M6RMTNEfF8ROwYM6/lfhCV6yOiERGPRsTpeS1vvwn64pr6b+TRiPhqRBwz5rlP1n3xjYj4UE6rp0ervhjz3McjokTEgvrxpPcLQ7hyHbCxlHIycCrwOPDHwD2llEXAPfXjWS0i+oHLgcWllPcCPcBHgU8D19Z98V3gkrxWTqu1wJJx8ybaD5YCi+rbpcBnOtTGTlnLG/viLuC9pZSfBr4JfBIgIn6Kaj85pV7nxojo6VxTp91a3tgXRMQJwLnAd8bMnvR+0fUhHBFHAR8E/g6glPLDUsr3gAuAdfVi64Bfz2lhx/UC8yKiF5gPPAP8EnBr/fys7YtSyibgxXGzJ9oPLgDWl8qDwDERcXxnWjr9WvVFKeXfSikj9cMHgXfV0xcA/1hKebWU8iTQAN7fscZOswn2C4BrgU8AY7/dMOn9outDGHgPsAv4+4h4JCI+FxFvBRaWUp4BqO/fmdnITiil7AT+kuo/+zPAS8AW4Htj/vieBvpzWphiov2gH3hqzHLd1i+/A2yop7uuLyLifGBnKWXbuKcm3ReGcHXkdzrwmVLKzwC76YJTD63U5zsvAE4EfhR4K9Xbq/H8XiNEi3ld0S8RcSUwAnyxOavFYrO2LyJiPnAlcFWrp1vMO2BfGMLVf6qnSykP1Y9vpQrl55pvI+r755Pa10nnAE+WUnaVUvYCXwHOonpL1Vsv8y7gf7MamGCi/eBp4IQxy3VFv0TEcuBXgY+V0R8ZdFtfnER1oLItIoaoXu/XI+JHOIy+6PoQLqU8CzwVET9Rz/pl4L+A24Hl9bzlwL8kNK/TvgOcERHzIyIY7Yt7gQ/Xy3RLXzRNtB/cDiyrPw0/A3ipedpitoqIJcAfAeeXUvaMeep24KMRcUREnEj1odTDGW3shFLK9lLKO0spA6WUAargPb3OksnvF6WUrr8BpwGbgUeB24B3AH1Un4Z/q74/NrudHeqLPwWeAHYAnweOoDpv/jDVBy5fBo7Ibuc0vfZbqM6F763/sC6ZaD+getv518B/A9upvlGS/hqmuS8aVOc7t9a3vxmz/JV1X3wDWJrd/unui3HPDwELDne/8GfLkpSo609HSFImQ1iSEhnCkpTIEJakRIawJCUyhCUpkSGsjoiI8yNixv8cvB7W9PfaWN6fjHv8QLvK1uzg94R1SCKip5SyL7sd0y0iBoB/LdVQnoey/AH7JSJeLqW8rU3N0yzkkbCIiIF6sO519UDUt9Y/XR6KiKsi4j7gonHrvD8iHqhHnnug+bPviPiDiLi5nn5fPTj8/Ii4OCJuqOdfVM/fFhGbDtKu/4iIr9e3s+r5x0fEpojYWpfzC/X8lyPir+pl74mI4+r5J0XExojYUpd3cj1/YT04+bb6dhZwNXBSXfY1E7Tr7Ii4NyL+gepXUUTEbXX5j0XEpfW8q6mGBd0aEV9strG+j6gGSd8REdsj4iOHtfH05pf9k0Bv+TdggGqkp5+rH98MfJzq55ifmGCdo4Deevoc4J/r6TnAJuBCqp+CN8u8GLihnt4O9NfTxxygXfOBI+vpRcDmevoPgSvr6R7g7fV0oRpYBqoRrpr13QMsqqc/AHytnv4S8Ptjyjm67osdB+mvs6lG2ztxzLzmz5nnUf3ku69+/PK4dV+u73+DapD0HmAh1bgdx2fvC946f2uOjCU9VUq5v57+AtUVNqAKqlaOBtZFxCKq8JsLUErZHxEXU43D8bdjyhzrfmBtRPwT1UhtE5kL3BARpwH7gB+v5/8ncHNEzAVuK6VsrefvH9PeLwBfiYi3UY0E9+VqTCKgGg8DqsHql9Xt3ge8FId+GauHSzWAedPlEXFhPX0C1T+N4QOs//PALXW9z0XEvwM/SzUAjLqIpyPUNP7Dgebj3RMs/yng3lKdO/014Mgxzy0CXqYak/iNFZVyGbCKKqy2RkTfBHVcATxHdcmpxcBb6vU3UV0NZSfw+YhYdoDXNIdqUPrTxtx+coLlJ+O1fomIs6neDZxZSjkVeITX90crrcadVRcyhNX0YxFxZj39m8B9B1n+aKoQhOpUAwARcTTVNfs+CPRFxIfHrxgRJ5VSHiqlXAW8wOvHXx1fxzOllP3Ab1O9dSci3g08X0r5LNVlqZoXU5zD6JCbvwXcV0r5PvBkRFxUrxsRcWq9zD3A79bze6K61NUPgLcf5LW3aud3Syl76vPNZ4x5bm99xD7eJuAjdb3HUfXXrB3+URMzhNX0OLA8Ih4FjqXFBQoj4rKIuKx++BfAn0fE/dThWLsWuLGU8k2q4Q+vjojxl4a6pv4wagdVGI2/REzTjXWbHqQ6FdE8+jyb6gj6Eapzq9fV83cDp0TEFqpTDX9Wz/8YcElEbAMeo7p6CMBK4BcjYjvVZZxOKaUMA/fXH5i1/GCuhY1Ab913n6K6/lrTTcCjzQ/mxvgq1SmbbcDXqM69P3uI9WkW8StqmvTXsmYqvw6mNyOPhCUpkUfCShcRHwI+PW72k6WUC1st3ykR8T6qq4uM9Wop5QMZ7dHsZAhLUiJPR0hSIkNYkhIZwpKUyBCWpET/D87PgvINmVuBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's check if 'radius_ratio' variable has any outliers as this will impact the replacement value of nan\n",
    "sns.boxplot(vehicles['pr.axis_aspect_ratio'])\n",
    "#Boxplot shows the presence of outliers for pr.axis_aspect_ratio (values>78 can be considered as outliers in below graph)\n",
    "#So we should replace outlier values with median first and then replace nan with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3878,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.0"
      ]
     },
     "execution_count": 3878,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"pr.axis_aspect_ratio\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3879,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing outliers with median value\n",
    "vehicles[\"pr.axis_aspect_ratio\"]=np.where(vehicles[\"pr.axis_aspect_ratio\"]>78, 61.0,vehicles[\"pr.axis_aspect_ratio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3880,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"pr.axis_aspect_ratio\"].fillna((vehicles[\"pr.axis_aspect_ratio\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3881,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72., 57., 66., 63., 61., 50., 65., 62., 55., 68., 58., 71., 52.,\n",
       "       69., 64., 59., 67., 54., 73., 51., 70., 53., 56., 74., 60., 76.,\n",
       "       75., 49., 47., 48.])"
      ]
     },
     "execution_count": 3881,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"pr.axis_aspect_ratio\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3882,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9fcc7510>"
      ]
     },
     "execution_count": 3882,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['pr.axis_aspect_ratio'],rug=True)\n",
    "#Variable 'pr.axis_aspect_ratio' is symmetrically distributed across the data set, has multiple modes\n",
    "#concentration can be seen between range of 47-76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3883,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9fe39c10>"
      ]
     },
     "execution_count": 3883,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-pr.axis_aspect_ratio vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['pr.axis_aspect_ratio'])\n",
    "#Inference: From below graph, it is evident that pr.axis_aspect_ratio is on lower range for each class. \n",
    "#Also bus has slightly wide spread range w.r.t pr.axis_aspect_ratio\n",
    "#and outliers are observed for van and bus w.r.t this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3884,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-'max.length_aspect_ratio' in below steps.\n",
    "#From previous anaylysis we could see that no nan values present in this variable. Lets reconfirm it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3885,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9, 52,  6,  7, 11,  5,  8, 49, 12, 22, 48,  4, 13,  3, 43, 25,\n",
       "       46, 19,  2, 55])"
      ]
     },
     "execution_count": 3885,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['max.length_aspect_ratio'].unique()\n",
    "#Confirmed that no nan values present here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3886,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9ff52150>"
      ]
     },
     "execution_count": 3886,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['max.length_aspect_ratio'],rug=True)\n",
    "#Inference: Data is highly right skewed, the presence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3887,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9ff4c7d0>"
      ]
     },
     "execution_count": 3887,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEHCAYAAACQkJyuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAQBUlEQVR4nO3df2xd5X3H8c8nMYUQSgoJRMWwGmS2FimMDo+xQhGwUKWhGkWtxiYkUqka+xkCWzV1azTtR7SVrRplUTvBGEuyUTattF3HspTAQLS0IzgUkgAZ3FGjERg/DKX8yAAn3/1xHocbz3b841x/fa/fL+nqnvP4/HienCcfHz/2ea4jQgCAmTcvuwIAMFcRwACQhAAGgCQEMAAkIYABIEnXZDZesmRJ9PT0tKgqANCZtm/f/mJEHDeyfFIB3NPTo/7+/vpqBQBzgO2nRitnCAIAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCST+ky4drJ+/Xo1Go2Dyvbs2SNJ6u7unvBxent7tXr16lrrBgBSBwdwo9HQQ7se074jjz1QNv+NVyRJ//PmxJo9/42XWlI3AJA6OIAlad+Rx2rv+1ceWF+we7MkHVQ2nuHtAaAVGAMGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkqQG8fv16rV+/PrMKLTcX2ghgaroyT95oNDJPPyPmQhsBTA1DEACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIMiMBPDg4qKuuukqDg4MzcTqMoY7r0Gg0dPHFF6vRaKTWA61R17WZzHHG2raOvjbb++uMBPDGjRu1c+dObdq0aSZOhzHUcR3WrVun119/XevWrUutB1qjrmszmeOMtW0dfW2299eWB/Dg4KC2bNmiiNCWLVu460lSx3VoNBoaGBiQJA0MDEzproL+MHvVdW0mc5yxtq2jr7VDf+2q9Wij2Lhxo/bv3y9J2rdvnzZt2qRrrrlGkrRnzx7t3btXa9asqf28jUZD896KaR1j3v/+SI3Gq9OqX6PR0IIFC6ZVjzqMdx0mauRdxLp167Rhw4YZrwdao65rM5njjLVtHX2tHfrrIe+AbV9pu992/wsvvDDpE9x5550aGhqSJA0NDWnr1q2TryWmrY7rMHw3Mdb6TNUDrVHXtZnMccbato6+1g799ZB3wBFxo6QbJamvr2/St5TLly/X5s2bNTQ0pK6uLl100UUHvtbd3S1Juv766yd72ENas2aNtj/53LSOsf+Io9V7ytJp1a8Vd/dTMd51mKienp6DOnFPT09KPdAadV2byRxnrG3r6Gvt0F9bPga8atUqzZtXnWb+/Pm64oorWn1KjKKO67B27dpx12eqHmiNuq7NZI4z1rZ19LV26K8tD+DFixdrxYoVsq0VK1Zo8eLFrT4lRlHHdejt7T1wF9HT06Pe3t6UeqA16ro2kznOWNvW0dfaob/OyJ+hrVq1SsuWLeNuJ1kd12Ht2rVauHDhlO4m6qwHWqOuazOZ44y1bR19bbb3V0dMfFi3r68v+vv7azv58PhoK8eA975/5YGyBbs3S9JBZeNZsHuzzqxpDLgVbQTQHmxvj4i+keU8igwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJF2ZJ+/t7c08/YyYC20EMDWpAbx69erM08+IudBGAFPDEAQAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQhAAGgCQEMAAkIYABIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAAaAJF3ZFWil+W+8pAW7NzetD0rSQWWH2l9a2oqqAUDnBnBvb+//K9uzZ0iS1N090VBdOupxAKAOHRvAq1evzq4CAIyLMWAASEIAA0ASAhgAkhDAAJCEAAaAJAQwACQhgAEgCQEMAEkIYABIQgADQBICGACSEMAAkIQABoAkBDAAJCGAASAJAQwASQhgAEhCAANAEgIYAJIQwACQxBEx8Y3tFyQ9VVaXSHqxFZWaZeZCO+dCG6W50c650Eap/dr5vog4bmThpAL4oB3t/ojom3a1Zrm50M650EZpbrRzLrRR6px2MgQBAEkIYABIMp0AvrG2Wsxuc6Gdc6GN0txo51xoo9Qh7ZzyGDAAYHoYggCAJAQwACSZUgDbXmH7P203bH+27kplsX2z7edt72oqO9b2VttPlPdjMus4XbZPsn237cdsP2J7TSnvmHbaPsL2NtsPlzb+YSk/2fb9pY3/aPtd2XWdLtvzbX/f9u1lvRPbOGB7p+2HbPeXso7or5MOYNvzJX1J0kclnSbpl2yfVnfFkmyQtGJE2Wcl3RURp0q6q6y3syFJvx0RH5B0tqTfKNevk9r5pqQLI+InJZ0haYXtsyVdK+m60saXJX06sY51WSPpsab1TmyjJF0QEWc0/e1vR/TXqdwBnyWpERFPRsRbkv5B0iX1VitHRNwr6aURxZdI2liWN0r6+IxWqmYR8WxEPFiWX1X1n7dbHdTOqLxWVg8rr5B0oaSvlvK2bqMk2T5R0sWSbirrVoe1cRwd0V+nEsDdkv67af3pUtaplkbEs1IVXpKOT65PbWz3SPqgpPvVYe0sP5o/JOl5SVsl/ZekH0bEUNmkE/rtFyX9jqT9ZX2xOq+NUvXN8w7b221fWco6or92TWEfj1LG37K1GdtHSbpN0tUR8aPq5qlzRMQ+SWfYfo+kr0v6wGibzWyt6mP7Y5Kej4jtts8fLh5l07ZtY5NzIuIZ28dL2mp7d3aF6jKVO+CnJZ3UtH6ipGfqqc6s9Jzt90pSeX8+uT7TZvswVeF7S0R8rRR3XDslKSJ+KOkeVePd77E9fNPR7v32HEk/b3tA1TDgharuiDupjZKkiHimvD+v6pvpWeqQ/jqVAH5A0qnlt63vkvSLkr5Zb7VmlW9KWlWWV0n658S6TFsZJ/wbSY9FxF80falj2mn7uHLnK9sLJC1XNdZ9t6RPls3auo0R8bsRcWJE9Kj6P/jvEXG5OqiNkmR7oe13Dy9L+oikXeqU/hoRk35JWinpcVXjap+byjFm40vSrZKelfS2qjv9T6saV7tL0hPl/djsek6zjeeq+rF0h6SHymtlJ7VT0umSvl/auEvS75fyUyRtk9SQ9E+SDs+ua03tPV/S7Z3YxtKeh8vrkeG86ZT+yqPIAJCEJ+EAIAkBDABJCGAASEIAA0ASAhgAkhDAAJCEAMak2e5pnrKzxuOeb/tDTesbbH9yvH2y2f69Go91te0jm9Y3Dz9Qgs5EAGM2OV/Shw610Swz4QB2Zbz/c1dLOhDAEbEyqkep0aEI4A5U7lB3277J9i7bt9hebvu+MoH1WeX13TKZ93dt/0TZ97ds31yWl5X9jxznXPNt/7ntB2zvsP0rpfx82/fY/mqpyy3lMWjZXlnKvmP7L23fXmZm+1VJ15SJtz9cTnFeqd+T490N2z7K9l22HyyTd19Syhfa/ldXk7Pvsn1ZKR+wfa2ridu32e4t5cfZvq205wHb5zQd/2/LsXfY/oTtz0taUOp7yzjX4jHbX5b0oKSTbP+V7X4fPFn8VZJOkHS37bub6rik6brsKq+rD9UH0CayH8XjVf9LUo+qideXqfomu13Szapmy7pE0jckHS2pq2y/XNJtZXmepHslXSqpX9VMVKMdf1dZvlLS2rJ8eNnnZFV3s6+omhBmnqTvqXoM+ghV05meXPa5Ve88RvsHkj7TdJ4Nqh6nnadq8v/GOG3uknR0WV6i6lFcS/qEpL9u2m5ReR/QO4+1XtFUh69IOrcs/5iqOTOkaqLzLzYd55jy/toErsV+SWc3lR1b3uermijo9KY6LWnabqC05UxJOyUtlHSUqkdyP5jdz3hN/zWV6SjRHn4QETslyfYjqj49IGzvVBUKiyRttH2qqrkhDpOkiNhv+1Oq5lG4ISLuO8R5PiLp9Ka700WSTpX0lqRtEfF0qcND5byvSXoyIn5Qtr9VVYiP5RsRsV/So7aXjrOdJf2J7fNUBV63pKWqgusLtq9VFbLfbtrn1qb368ryckmn+Z3pOY8uk8EsVzXpjSQpIl4epy4jPRUR/9G0/guu5rXtkvReVd9cdoyz/7mSvh4Rr0uS7a9J+rCq+S7QxgjgzvVm0/L+pvX9qq77H0u6OyIuLT/+39O0/amqgvKECZzHklZHxLcOKqzmqG2uw75y3slOPNx8jPH2vVzScZLOjIi3XU3TeEREPG77TFUTDv2p7Tsi4o/KPs0ToQwvz5P0sxGxd0R7rKnPrft603FOlvQZST8dES/b3qDqp4LxdNZkzTiAMeC5a5GkPWX5U8OFthdJul7SeZIWT+CvEL4l6ddczTEs2z/uatrAseyWdEoJfUm6rOlrr0p69wTrP9IiVROUv237AknvK/U5QdIbEfH3kr4g6aea9rms6f17ZfkOSb85vIHtM8YoH/4QyLeH2z5BR6sK5FfKHf1Hm742VvvvlfRx20eWf9tLJX17lO3QZgjguevPVN0R3qdqLHLYdZK+HBGPq5qO8/O2j7fdZ/umUY5zk6RHJT3o6k/TbtA4P1mVO8tfl7TF9nckPadqrFiS/kXSpSN+CTdRt0jqc/WpuZerCnqpGgffVoZAPidpXdM+h9u+X9UHW15Tyq4qx9lh+1FVvxhU2e+Y8kuwhyVdUMpvlLRjrF/CjRQRD6saOnhE1bh88xDPjZL+bfiXcE37PKhqPHybqo+PuikiGH7oAExHiRln+6iIeK38WP8lSU9ExHWH2q/mOgxI6ouIF2fyvEAz7oCR4ZfLHekjqoYObkiuD5CCO2C0FdvLJP3diOI3I+JnMuozzPbwJzSM9HMRMTjT9UF7IIABIAlDEACQhAAGgCQEMAAkIYABIMn/AUAiD78dBiaxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's reconfirm our understanding\n",
    "sns.boxplot(vehicles['max.length_aspect_ratio'])\n",
    "#So the presence of outliers is confirmed. max.length_aspect_ratio> 15 can be treated as outliers. Lets replace it with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3888,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 3888,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['max.length_aspect_ratio'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3889,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"max.length_aspect_ratio\"]=np.where(vehicles[\"max.length_aspect_ratio\"]>15, 8,vehicles['max.length_aspect_ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3890,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa020e090>"
      ]
     },
     "execution_count": 3890,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['max.length_aspect_ratio'])\n",
    "#Reconfirming that outliers are replaced with median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3891,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa03279d0>"
      ]
     },
     "execution_count": 3891,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-max.length_aspect_ratio vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['max.length_aspect_ratio'])\n",
    "#Inference: From below graph, it is evident that max.length_aspect_ratio is on lower range for each class. \n",
    "#Also bus has narrow spread range w.r.t max.length_aspect_ratio\n",
    "#and outliers are observed for van and bus w.r.t this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3892,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analysis of variable-scatter_ratio\n",
    "#From previous analysis, we sensed presence of nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3893,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([162., 149., 207., 144., 255., 153., 137., 122., 183., 133., 123.,\n",
       "       152., 174., 204., 118., 177., 216., 208., 154., 150., 143., 147.,\n",
       "       128., 218., 192., 146., 155., 140., 142., 164., 157., 151., 205.,\n",
       "       119., 158., 213., 159., 130., 148., 156., 163., 210., 257., 185.,\n",
       "       209., 193., 184., 225., 190., 215., 224., 176., 126., 195., 172.,\n",
       "       127., 261., 171., 125., 169., 197., 145., 214., 201., 114., 194.,\n",
       "       220., 221., 173., 134., 160., 121., 199., 186., 161., 222., 136.,\n",
       "       187., 250., 202., 166., 132., 211., 247., 116., 203., 181., 240.,\n",
       "       219., 212., 138., 131., 117., 112., 165.,  nan, 226., 129., 175.,\n",
       "       135., 217., 200., 241., 198., 120., 260., 188., 170., 234., 236.,\n",
       "       168., 167., 227., 139., 256., 206., 141., 238., 178., 223., 252.,\n",
       "       239., 115., 191., 179., 251., 262., 196., 180., 189., 237., 265.])"
      ]
     },
     "execution_count": 3893,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets reconfirm our understanding\n",
    "vehicles['scatter_ratio'].unique()\n",
    "#Yes, nan is one of the unique value present in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3894,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa04314d0>"
      ]
     },
     "execution_count": 3894,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Before replacing nan value, let us also check that if outliers are present\n",
    "sns.boxplot(vehicles['scatter_ratio'])\n",
    "#No outliers are present. We can replace nan values with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3895,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing nan with median\n",
    "vehicles[\"scatter_ratio\"].fillna((vehicles[\"scatter_ratio\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3896,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([162., 149., 207., 144., 255., 153., 137., 122., 183., 133., 123.,\n",
       "       152., 174., 204., 118., 177., 216., 208., 154., 150., 143., 147.,\n",
       "       128., 218., 192., 146., 155., 140., 142., 164., 157., 151., 205.,\n",
       "       119., 158., 213., 159., 130., 148., 156., 163., 210., 257., 185.,\n",
       "       209., 193., 184., 225., 190., 215., 224., 176., 126., 195., 172.,\n",
       "       127., 261., 171., 125., 169., 197., 145., 214., 201., 114., 194.,\n",
       "       220., 221., 173., 134., 160., 121., 199., 186., 161., 222., 136.,\n",
       "       187., 250., 202., 166., 132., 211., 247., 116., 203., 181., 240.,\n",
       "       219., 212., 138., 131., 117., 112., 165., 226., 129., 175., 135.,\n",
       "       217., 200., 241., 198., 120., 260., 188., 170., 234., 236., 168.,\n",
       "       167., 227., 139., 256., 206., 141., 238., 178., 223., 252., 239.,\n",
       "       115., 191., 179., 251., 262., 196., 180., 189., 237., 265.])"
      ]
     },
     "execution_count": 3896,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scatter_ratio\"].unique()\n",
    "#Confirmed that replacement is successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3897,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa04fe110>"
      ]
     },
     "execution_count": 3897,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['scatter_ratio'],rug=True)\n",
    "#Inference: scatter_ratio has multiple modes and \n",
    "#concentration can be found between value ranges 110-240 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3898,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0693c10>"
      ]
     },
     "execution_count": 3898,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAbP0lEQVR4nO3de5RV5Z3m8e8DokCjIUqpSEHKEchEbRNNaZuxTRt74SUqLFei0SRK20mYJESxc20vbdSla9IazUol087Q46XssXXwkoR0S2vp0qhpwZTKRSSRmha1hCjgeOWiwG/+2Lt076KoqqNnn82p83zWqlVnv3vvw684a9VT7373fl9FBGZmZj2GlV2AmZntXBwMZmaW42AwM7McB4OZmeU4GMzMLGeXsgv4oMaNGxctLS1ll2FmVlcef/zxdRHR1Ne+ug+GlpYWOjs7yy7DzKyuSHpuR/t8KcnMzHIcDGZmluNgMDOzHAeDmZnlOBjMzHpZt24d5557LuvXry+7lFI4GMzMemlvb2fp0qW0t7eXXUopHAxmZhnr1q1jwYIFRAQLFixoyF6Dg8HMLKO9vZ2e5Qi2bdvWkL0GB4OZWUZHRwfvvPMOAO+88w733ntvyRXVnoPBzCxj2rRpSAJAEscdd1zJFdWeg8HMLOOUU05591JSRDB9+vSSK6o9B4OZWcbtt9+e2543b15JlZSn0GCQNFHSA5JWSFouaU5m37mS/pC2X5Vpv0BSV7rv+CLrMzPr7f77789t33fffSVVUp6iZ1fdAnwnIp6QtDvwuKQOYB9gBnBIRGyWtDeApAOBM4CDgP2A+yRNjYitBddpZgbw7mWkHW03gkJ7DBGxJiKeSF+/AawAJgDfAH4UEZvTfS+np8wAbouIzRHxLNAFHFFkjWZmWePHj+93uxHUbIxBUgtwKLAImAocLWmRpN9IOjw9bALwQua07rSt93vNktQpqXPt2rXFFm5mDaX3A21+wK0gksYAdwLnR8TrJJewPgwcCXwPmKfk/jD1cfp2/biImBsRrRHR2tTU5wJEZmbvS+/bU48/vvGGOgsPBkkjSELhloi4K23uBu6KxGPANmBc2j4xc3ozsLroGs3Menz84x/PbR966KElVVKeou9KEnA9sCIirs3s+iVwbHrMVGBXYB0wHzhD0m6S9gemAI8VWaOZWdY111yT277qqqt2cOTQVfRdSUcBZwHLJC1O2y4EbgBukPQU8DYwM5Kh/+WS5gFPk9zRNNt3JJlZLb311lv9bjeCQoMhIh6h73EDgC/v4JwrgSsLK8rMzPrlJ5/NzDKGDRvW73YjaLyf2MysH9OmTcttexI9M7MGd9ppp+W2Tz/99JIqKY+Dwcwsw5PoORjMzHJ6T5rX0dFRUiXlcTCYmVmOg8HMLGPkyJH9bjcCB4OZWYYfcHMwmJnl7Lfffv1uNwIHg5lZRiMuzNObg8HMLGPNmjW57dWrG2+CZweDmVlGS0tLv9uNwMFgZpZx8cUX57YvueSSkiopj4PBzCxj6tSp7/YSWlpamDx5crkFlcDBYGbWy9lnnw3AOeecU3Il5XAwmJn1cvPNNwNw4403llxJOYpe2nOipAckrZC0XNKcXvu/KykkjUu3JalNUpekpZIOK7I+M7PennnmGVatWgXAqlWr6OrqKregEhTdY9gCfCciPgYcCcyWdCAkoQFMA57PHH8iyTrPU4BZwHUF12dmlnPFFVfkti+//PKSKilPocEQEWsi4on09RvACmBCuvsnwPeB7NMkM4CbI7EQGCtpfJE1mpll9fQWdrTdCGo2xiCpBTgUWCRpOvBiRCzpddgE4IXMdjfvBYmZWeFGjx7d73Yj2KUW/4ikMcCdwPkkl5cuAvpaL099tG33fLqkWSSXmpg0aVL1CjWzhrdp06Z+txtB4T0GSSNIQuGWiLgLOADYH1giaRXQDDwhaV+SHsLEzOnNwHbPo0fE3IhojYjWpqamon8EM2sg27Zt63e7ERR9V5KA64EVEXEtQEQsi4i9I6IlIlpIwuCwiPgjMB84O7076UjgtYhYs6P3NzOz6iv6UtJRwFnAMkmL07YLI+LuHRx/N/BZoAvYADTm0yVmVprhw4ezdevW3HajKTQYIuIR+h43yB7TknkdwOwiazIz68/IkSNzi/N4BTczswbnFdwcDGZmOWPGjOl3uxE4GMzMMrZs2dLvdiNwMJiZZRx//PG57RNOOKGkSsrjYDAzy5g5cya77JLclzNixAhmzpxZckW152AwM8sYN24cJ510EpI46aST2GuvvcouqeZqMiWGmVk9mTlzJqtWrWrI3gK4x2BmZr04GMzMemlvb2fp0qW0t7eXXUopHAxmZhnr1q1jwYIFRAQLFixg/fr1ZZdUcx5jsIbW1tZW2NKN3d3dADQ3N1f9vSdPnsx5551X9fe1pLfQM6Pq1q1baW9v59vf/nbJVdWWewxmBdm4cSMbN24suwyrUEdHx7sPtW3ZsoV777235Ipqzz0Ga2hF/tXd895tbW2F/RtWfUcffTT33HPPu9uf/vSnS6ymHO4xmJlZjoPBzCzj4Ycfzm0/9NBDJVVSHgeDmVnGtGnTSBafBEkcd1xfy9MPbQ4GM7OMU045hWTNMIgIpk+fXnJFtVf0ms8TJT0gaYWk5ZLmpO1XS/q9pKWSfiFpbOacCyR1SfqDpON3/O5mZtX361//OtdjmD9/fskV1V7RPYYtwHci4mPAkcBsSQcCHcDBEXEI8AxwAUC67wzgIOAE4B8kNd6Cq2ZWmo6OjlyPwberVllErAHWpK/fkLQCmBAR2f/phcDn09czgNsiYjPwrKQu4Ajg0SLrNLP6VMQDiqNGjWLDhg257Wre1lwPDyfWbIxBUgtwKLCo166/BhakrycAL2T2dadtvd9rlqROSZ1r166tfrFm1rD23Xffd19Lym03ipo84CZpDHAncH5EvJ5pv4jkctMtPU19nB7bNUTMBeYCtLa2brffzBpDUX95n3rqqaxfv54ZM2Y03HQYUINgkDSCJBRuiYi7Mu0zgZOBv4yeC3pJD2Fi5vRmYHXRNZqZZe27775s2rTJ6zEUQcnQ/vXAioi4NtN+AvADYHpEbMicMh84Q9JukvYHpgCPFVmjmVlvI0aMYMqUKQ25ehsU32M4CjgLWCZpcdp2IdAG7AZ0pLeFLYyIr0fEcknzgKdJLjHNjoitBddoZmYZRd+V9Ah9jxvc3c85VwJXFlaUmZn1y08+m5lZjoPBzMxyHAxmZpbjYDAzsxyv4GZ1oci1mYuycuVKoNhV4qqtHqZrsOI5GKwudHV18cxTTzBpTP3cvbzrO0mHfNOq35VcyeA8/6bnq7RERcEgaR/g8HTzsYh4ufolmfVt0pitXNz6ZtllDFlXdI4puwTbSQx6jEHS6SRPIZ8GnA4skvT5/s8yM7N6U0mP4SLg8J5egqQm4D7gjiIKMzOzclRyV9KwXpeO1ld4vpmZ1YFKegz/Juke4NZ0+wv0M7WFmZnVp0EHQ0R8T9LnSCbGEzA3In5RWGVmZlaKiu5Kiog7SdZWMDOzIWrAYJD0SET8uaQ3yK+mJiAiYo/CqjMzs5obMBgi4s/T77sXX46ZmZWtkucY/mkwbWZmVt8qud30oOyGpF2AT1a3HDMzK9uAwSDpgnR84RBJr6dfbwAvAb8a4NyJkh6QtELScklz0vY9JXVIWpl+/3DaLkltkrokLZV0WBV+RjMzq8CAwRAR/y0dX7g6IvZIv3aPiL0i4oIBTt8CfCciPgYcCcyWdCDwt8D9ETEFuD/dBjgRmJJ+zQKue38/lpmZvV+VPMdwQfqX/RRgZKb9oX7OWQOsSV+/IWkFMAGYARyTHtYOPAj8IG2/OSICWChprKTx6fuYmVkNDDoYJH0VmAM0A4tJegCPAscO8vwW4FBgEbBPzy/7iFgjae/0sAnAC5nTutO2XDBImkXSo2DSpEmD/RHMzGwQKhl8nkMy5fZzEfEZkl/yawdzoqQxJA/GnR8Rr/d3aB9tsV1DxNyIaI2I1qampsGUYGZmg1RJMGyKiE0AknaLiN8DHx3oJEkjSELhloi4K21+SdL4dP94oGdyvm5gYub0ZmB1BTWamdkHVEkwdEsaC/wS6JD0Kwb4pS1JwPXAioi4NrNrPjAzfT2T9+5umg+cnd6ddCTwmscXzMxqq5LB51PTl5dKegD4EPBvA5x2FHAWsEzS4rTtQuBHwDxJXwGeJ1n8B5LZWj8LdAEbgHMGW5+ZmVXHoIJB0jBgaUQcDBARvxnMeRHxCH2PGwD8ZR/HBzB7MO9tZmbFGNSlpIjYBiyR5FuAzMyGuEqm3R4PLJf0GPBWT2NETK96VWZmVppKguGywqowM7OdRiWDz/2OK0h6NCI+9cFLMjOzMlVyu+pARg58iJmZ7eyqGQzbPaFsZmb1p5rBYGZmQ0A1g2FHzyuYmVkdGVQwSBou6b4BDjurCvWYmVnJBvuA21Zgg6QP9XPMU1WryszMSlPJcwybSOY86iD/gNt5Va/KzMxKU0kw/Gv6ZWZmQ1glD7i1SxoFTIqIPxRYk5mZlaiSpT1PAX4M7ArsL+kTwOWeK8lqobu7m7feGM4VnWPKLmXIeu6N4fxJd3fZZdhOoJLbVS8FjgBeBYiIxcD+BdRkZmYlqmSMYUtEvJYsyvYuP+1sNdHc3MymLWu4uPXNsksZsq7oHMPI5uayy7CdQCXB8JSkLwLDJU0BzgP+vZiyzMysLJVcSjoXOAjYDPwz8Bowp78TJN0g6WVJT2XaPiFpoaTFkjolHZG2S1KbpC5JSyUdVvmPY2ZmH1QlwXBSRFwUEYenXxcDAw083wSc0KvtKuCyiPgEcEm6DXAiMCX9mgVcV0FtZmZWJZUEwwWDbHtXRDwEvNK7Gdgjff0hYHX6egZwcyQWAmMlja+gPjMzq4IBxxgknQh8FpggqS2zaw9gy/v4N88H7pH0Y5Jg+i9p+wTghcxx3Wnbmj5qmkXSq2DSJC9DbWZWTYPpMawGOkmmxHg88zUfOP59/JvfAP4mIiYCfwNcn7b3NTtrn3c9RcTciGiNiNampqb3UYKZme3IgD2GiFgCLJG0T0S0Z/dJmgP8tMJ/cybvDVrfDvyv9HU3MDFzXDPvXWYyszrV1tZGV1dX2WVUZOXKlQCcd159TQU3efLkqtRcye2qZ/DeQHGPv6LyYFgN/AXwIHAssDJtnw98S9JtwJ8Br0XEdpeRzKy+dHV18eTyJ2Fs2ZVUYFvy7ckXnyy3jkq8Wr23GswYw5nAF0mmwZif2bU7sH6Ac28FjgHGSeoGfgh8DfippF1ILk/NSg+/m2QsowvYAJxT0U9iZjuvsbDtmG1lVzGkDXuweuuuDabH8O8kA8DjgGsy7W8AS/s7MSLO3MGuT/ZxbACzB1GPmZkVaDBjDM8BzwGfKr4cMzMr26D7HpKOlPQ7SW9KelvSVkmvF1mcmZnVXiUXpX4OnEkyWDwK+CrwsyKKMjOz8lRyVxIR0SVpeLoG9I2SPImemdkQU0kwbJC0K7BY0lUkA9J/UkxZZmZWlkouJZ2VHv8t4C2Sh9E+V0RRZmZWnkp6DOuAtyNiE3CZpOHAbsWUZWZmZamkx3A/MDqzPQq4r7rlmJlZ2SoJhpER8e66iunr0f0cb2ZmdaiSYHgru6qapFZgY/VLMjOzMlUyxjAHuF3SapLpsPcDvlBIVWZmVppKgmF/4FBgEnAqcCQ7WC/BzMzqVyWXkv4uIl4nmTx3GjAXr8tsZjbkVBIMW9PvJwH/IyJ+Bexa/ZLMzKxMlQTDi5L+J3A6cLek3So838zM6kAlYwynAycAP46IVyWNB75XTFn1pailC7u7uwFobm6u+ntXawlAMxt6Bh0MEbEBuCuzvYZkviQryMaNvhvYzGqvotlVKyXpBuBk4OWIODjTfi7JnEtbgH+NiO+n7RcAXyEZzzgvIu4psr5qKeov7573bWtrK+T9zcz6UmgwADeRrONwc0+DpM8AM4BDImKzpL3T9gOBM4CDSJ6RuE/S1HSKbzMzq5FCB48j4iHglV7N3wB+FBGb02NeTttnALdFxOaIeBboAo4osj4zM9teGXcVTQWOlrRI0m8kHZ62TwBeyBzXnbZtR9IsSZ2SOteuXVtwuWZmjaWMYNgF+DDJk9PfA+ZJEqA+ju3zyeqImBsRrRHR2tTUVFylZmYNqIxg6AbuisRjwDZgXNo+MXNcM7C6hPrMzBpaGcHwS+BYAElTSZ6eXgfMB86QtJuk/YEpwGMl1Gdm1tCKvl31VuAYYJykbuCHwA3ADZKeAt4GZkZEAMslzQOeJrmNdbbvSDIzq71CgyEiztzBri/v4PgrgSuLqKWop5OLtHLlSqC45ySK4qeqzepb0c8x7DS6urp4ctnTbBu9Z9mlDJreTsbeH/+/fyy5ksEbtqH33clmVm8aJhgAto3ek00Hnlx2GUPayKf/pewSzOwD8uyoZmaW01A9Bqtvz785nCs6x5RdxqC9tCH5u2uf0dtKrmRwnn9zOFMLeN/u7m54DYY96L9DC/UqdEd3Vd7KwWB1YfLkyWWXULG305sHRrZMKbmSwZlKff4/W/U5GKwu1ONdTp4dN9Hc3MxarWXbMfXRc6pXwx4cRvOE6qzd4r6dmZnluMdgZsV7tc7GGN5Mv9fPkBa8yg6mHa2cg8HMClWP4xY9D5dOmVAf40MATKje/7WDwcwK5fGh+lNHfTszM6sFB4OZmeU4GMzMLMfBYGZmOQ4GMzPLcTCYmVlOw9yu2t3dzbANr3la6IIN27Ce7u4tZZdhZh9A0Ut73gCcDLwcEQf32vdd4GqgKSLWSRLwU+CzwAbgryLiiSLrM7P6VtTKjEWunlgPKxwW3WO4Cfg5cHO2UdJEYBrwfKb5RGBK+vVnwHXp96pobm7mpc27eKGego18+l9obt637DLMPpBRo0aVXUKpil7z+SFJLX3s+gnwfeBXmbYZwM0REcBCSWMljY+INUXWaGb1a2f/y7te1XzwWdJ04MWIWNJr1wTghcx2NzuYEkrSLEmdkjrXrl1bUKVmZo2ppsEgaTRwEXBJX7v7aIu+3ici5kZEa0S0NjU1VbNEM7OGV+u7kg4A9geWJGPNNANPSDqCpIcwMXNsM7C6xvWZmTW8mvYYImJZROwdES0R0UISBodFxB+B+cDZShwJvObxBTOz2is0GCTdCjwKfFRSt6Sv9HP43cB/AF3APwLfLLI2MzPrW9F3JZ05wP6WzOsAZhdZj5mZDcxTYpiZWY6DwczMchwMZmaW42AwM7OchpldFWDYhlfqanZVbXodgBi5R8mVDN6wDa8AnivJrJ41TDBMnjy57BIqtnLlGwBMOaCeftHuW5f/12b2noYJhnqcbKun5ra2tpIrMbNG4jEGMzPLcTCYmVmOg8HMzHIcDGZmluNgMDOzHAeDmZnlOBjMzCzHwWBmZjkOBjMzyyl6BbcbJL0s6alM29WSfi9pqaRfSBqb2XeBpC5Jf5B0fJG1mZlZ34ruMdwEnNCrrQM4OCIOAZ4BLgCQdCBwBnBQes4/SBpecH1mZtZLocEQEQ8Br/RquzcitqSbC4Hm9PUM4LaI2BwRz5Ks/XxEkfWZmdn2yh5j+GtgQfp6AvBCZl932mZmZjVUWjBIugjYAtzS09THYbGDc2dJ6pTUuXbt2qJKNDNrSKUEg6SZwMnAlyKi55d/NzAxc1gzsLqv8yNibkS0RkRrU1NTscWamTWYmgeDpBOAHwDTI2JDZtd84AxJu0naH5gCPFbr+szMGl2hC/VIuhU4BhgnqRv4IcldSLsBHZIAFkbE1yNiuaR5wNMkl5hmR8TWIuszM7PtFRoMEXFmH83X93P8lcCVxVVkZmYDKfuuJDMz28k0zJrPRWpra6Orq6vq77ty5UqgmPWqJ0+eXJfrYJtZ8RwMO7FRo0aVXYKZNSAHQxX4L28zG0ocDNbQiroMCL4UaPXLwWBWEF8KtHrlYLCG5r+6zbbn21XNzCzHwWBmZjkOBjMzy3EwmJlZjoPBzMxyHAxmZpbjYDAzsxwHg5mZ5ei9lTXrk6S1wHNl11GgccC6souw982fX/0a6p/dRyKiz7WR6z4YhjpJnRHRWnYd9v7486tfjfzZ+VKSmZnlOBjMzCzHwbDzm1t2AfaB+POrXw372XmMwczMctxjMDOzHAeDmZnlOBjMrCFJapH0VNl17IwcDGYlkOTVE22n5WAokaS/l/TNzPalkn4o6X5JT0haJmlGuq9F0gpJ/yhpuaR7JXlR4Z2ApLMlLZW0RNI/STpF0iJJT0q6T9I+6XGXSpor6V7g5pLLtsQuktrTz+8OSaMlrZI0DkBSq6QH09d/IWlx+vWkpN1LrbxADoZy3QZ8IbN9OnAjcGpEHAZ8BrhGktL9U4D/HhEHAa8Cn6tlsbY9SQcBFwHHRsTHgTnAI8CREXEoyWf8/cwpnwRmRMQXa16s9eWjwNyIOAR4HfhmP8d+F5gdEZ8AjgY21qC+Urg7W6KIeFLS3pL2A5qA/wesAX4i6dPANmACsE96yrMRsTh9/TjQUuOSbXvHAndExDqAiHhF0p8C/0fSeGBX4NnM8fMjYsj+QqlDL0TEb9PX/xs4r59jfwtcK+kW4K6I6C68upK4x1C+O4DPk/QcbgO+RBISn0z/MnkJGJkeuzlz3lYc7DsDAb0fBvoZ8POI+FPgv/Le5wfwVq0Ks0Hp/dkFsIX3fje++9lFxI+ArwKjgIWS/nNNKiyBg6F8twFnkITDHcCHgJcj4h1JnwE+UmZxNqD7gdMl7QUgaU+Sz/DFdP/MsgqzQZkk6VPp6zNJLgOuIrnkB5nLtZIOiIhlEfH3QCfgYLBiRMRyYHfgxYhYA9wCtErqJOk9/L7M+qx/6ed3JfAbSUuAa4FLgdslPczQnrZ5KFgBzJS0FNgTuA64DPhp+vltzRx7vqSn0s95I7Cg5tXWiKfEMDOzHPcYzMwsx8FgZmY5DgYzM8txMJiZWY6DwczMchwMZh9QOgfSd8uuw6xaHAxmZpbjYDCrUO/ZVHvt+5qk36X77pQ0Om0/refhKEkPpW0HSXosna1zqaQpZfw8Zr35ATezCqSzqd4FHBUR69IpMM4D3oyIH0vaKyLWp8deAbwUET+TtAw4ISJelDQ2Il6V9DNgYUTcImlXYLgn2LOdgXsMZpXZbjbVXvsPlvRwGgRfAg5K238L3CTpa8DwtO1R4EJJPwA+4lCwnYWDwawyfc2mmnUT8K10ZtXLSGfnjIivAxcDE4HFac/in4HpJPPu3CPp2CILNxssB4NZZfqaTTVrd2CNpBEkPQbS4w6IiEURcQnJxHoTJf0n4D8iog2YDxxSk5/AbACez9+sAhGxXFLPbKpbgSdJpmnu8XfAIuA5YBlJUABcnQ4uiyRclgB/C3xZ0jvAH4HLa/JDmA3Ag89mZpbjS0lmZpbjYDAzsxwHg5mZ5TgYzMwsx8FgZmY5DgYzM8txMJiZWc7/Bx28GihYXlPSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-scatter_ratio vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['scatter_ratio'])\n",
    "#Inference: From below graph, it is evident that scatter_ratio is varies for each class. \n",
    "#Car has wide spread range w.r.t scatter_ratio\n",
    "#and outliers are observed for bus w.r.t this variable. Bus has right skewed distribution w.r.t scatter_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3899,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42., 45., 32., 46., 26., 48., 54., 36., 50., 43., 44., 37., 57.,\n",
       "       31., 55., 53., 33., 47., 40., 51., 34., 52., 35., 30., 38., 56.,\n",
       "       39., 58., 59., 49., 27., 41., nan, 61., 28., 29.])"
      ]
     },
     "execution_count": 3899,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysing the variable:elongatedness\n",
    "#Previous anaysis indicated presence of null values\n",
    "vehicles['elongatedness'].unique()\n",
    "#From below data, it is confirmed that nan is one of the unique value present in the data set under column 'elongatedness'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3900,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0506b50>"
      ]
     },
     "execution_count": 3900,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Before replacing nan values, we need to check if outliers are present\n",
    "sns.boxplot(vehicles['elongatedness'])\n",
    "#No outliers are present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3901,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing nan with median\n",
    "vehicles[\"elongatedness\"].fillna((vehicles[\"elongatedness\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3902,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42., 45., 32., 46., 26., 48., 54., 36., 50., 43., 44., 37., 57.,\n",
       "       31., 55., 53., 33., 47., 40., 51., 34., 52., 35., 30., 38., 56.,\n",
       "       39., 58., 59., 49., 27., 41., 61., 28., 29.])"
      ]
     },
     "execution_count": 3902,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"elongatedness\"].unique()\n",
    "#nan value is replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3903,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9efe6690>"
      ]
     },
     "execution_count": 3903,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['elongatedness'],rug=True)\n",
    "#Inference: elongatedness is spread across data set in multiple modes. \n",
    "#concentration exists between range of value 25-60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3904,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa09ec210>"
      ]
     },
     "execution_count": 3904,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-elongatedness vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['elongatedness'])\n",
    "#Inference: From below graph, it is evident that elongatedness varies for each class. \n",
    "#Car has wide spread range w.r.t elongatedness\n",
    "#Bus has left skewed distribution w.r.t elongatedness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3905,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., 19., 23., 28., 18., 17., 22., 21., 24., 25., nan, 27., 26.,\n",
       "       29.])"
      ]
     },
     "execution_count": 3905,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysing the variable-pr.axis_rectangularity\n",
    "#Let's reconfirm if this variable has nan values\n",
    "vehicles['pr.axis_rectangularity'].unique()\n",
    "#Shows the presence of nan value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3906,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/IPython/core/displayhook.py:276: UserWarning: Output cache limit (currently 1000 entries) hit.\n",
      "Flushing oldest 200 entries.\n",
      "  'Flushing oldest {cull_count} entries.'.format(sz=sz, cull_count=cull_count))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0ae2c90>"
      ]
     },
     "execution_count": 3906,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking if it has outliers\n",
    "sns.boxplot(vehicles['pr.axis_rectangularity'])\n",
    "#No outliers present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3907,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing nan with median\n",
    "vehicles[\"pr.axis_rectangularity\"].fillna((vehicles[\"pr.axis_rectangularity\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3908,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., 19., 23., 28., 18., 17., 22., 21., 24., 25., 27., 26., 29.])"
      ]
     },
     "execution_count": 3908,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"pr.axis_rectangularity\"].unique()\n",
    "#Replacement is successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3909,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0bc4ad0>"
      ]
     },
     "execution_count": 3909,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['pr.axis_rectangularity'],rug=True)\n",
    "#Inference: Variable is widespread and has multiple modes. \n",
    "#Also we can see discontinuity between values 20-21 and 24-25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3910,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0d53c10>"
      ]
     },
     "execution_count": 3910,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-pr.axis_rectangularity vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['pr.axis_rectangularity'])\n",
    "#Inference: From below graph, it is evident that pr.axis_rectangularity varies for each class. \n",
    "#Car has wide spread range w.r.t pr.axis_rectangularity\n",
    "#Bus has outliers w.r.t pr.axis_rectangularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3911,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([159, 143, 158, 144, 169, 146, 127, 130, 118, 148, 154, 166, 129,\n",
       "       139, 173, 145, 125, 142, 136, 165, 167, 151, 128, 150, 147, 156,\n",
       "       171, 162, 134, 160, 141, 163, 133, 168, 135, 161, 137, 178, 175,\n",
       "       186, 132, 138, 152, 153, 122, 157, 140, 174, 124, 164, 172, 170,\n",
       "       131, 123, 149, 126, 155, 176, 180, 119, 177, 121, 182, 179, 188,\n",
       "       120])"
      ]
     },
     "execution_count": 3911,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us analyze the variable-max.length_rectangularity if nan values and outliers present\n",
    "vehicles['max.length_rectangularity'].unique()\n",
    "#No nan values are present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3912,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0e4a410>"
      ]
     },
     "execution_count": 3912,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for outliers\n",
    "sns.boxplot(vehicles['max.length_rectangularity'])\n",
    "#No outliers present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3913,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0f29c50>"
      ]
     },
     "execution_count": 3913,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['max.length_rectangularity'],rug=True)\n",
    "#Data is almost symmetrically distributed across max.length_rectangularity and \n",
    "#concentration is found between value range 118-180 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3914,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa0f30550>"
      ]
     },
     "execution_count": 3914,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-max.length_rectangularity vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['max.length_rectangularity'])\n",
    "#Inference: From below graph, it is evident that max.length_rectangularity falls somewhat in same range for each class \n",
    "#Car has wide spread range w.r.t max.length_rectangularity\n",
    "#Bus has narrow spread and outliers w.r.t max.length_rectangularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3915,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176., 170., 223., 160., 241., 280., 162., 141., 202., 153., 148.,\n",
       "       180., 173., 196., 227., 137., 225., 175., 169., 164., 221., 143.,\n",
       "       229., 217., 168., 165., 232., 186., 174., 272., 235., 135., 178.,\n",
       "       191., 159., 172., 184., 181., 236., 275., 154., 222., 214., 145.,\n",
       "       203., 231., 208., 226., 210., 197., 171., 155., 278., 189., 142.,\n",
       "       218., 179., 166., 237.,  nan, 212., 177., 167., 132., 151., 216.,\n",
       "       219., 157., 224., 188., 161., 207., 156., 152., 220., 266., 228.,\n",
       "       185., 209., 204., 182., 200., 258., 146., 183., 163., 238., 194.,\n",
       "       134., 206., 136., 130., 190., 158., 147., 140., 265., 211., 138.,\n",
       "       247., 288., 234., 243., 256., 195., 213., 187., 205., 262., 320.,\n",
       "       285., 215., 150., 139., 267., 149., 193., 230., 254., 269., 264.,\n",
       "       199., 192., 144., 131., 246., 287., 240., 263.])"
      ]
     },
     "execution_count": 3915,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysis of variable-scaled_variance\n",
    "#Previous analysis indicated that nan values are present\n",
    "vehicles['scaled_variance'].unique()\n",
    "#nan is shwon as one of the unique value in data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3916,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa11ac950>"
      ]
     },
     "execution_count": 3916,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for outliers\n",
    "sns.boxplot(vehicles['scaled_variance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3917,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179.0"
      ]
     },
     "execution_count": 3917,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing outliers with median\n",
    "vehicles['scaled_variance'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3918,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"scaled_variance\"]=np.where(vehicles[\"scaled_variance\"]>290, 179,vehicles[\"scaled_variance\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3919,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"scaled_variance\"].fillna((vehicles[\"scaled_variance\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3920,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176., 170., 223., 160., 241., 280., 162., 141., 202., 153., 148.,\n",
       "       180., 173., 196., 227., 137., 225., 175., 169., 164., 221., 143.,\n",
       "       229., 217., 168., 165., 232., 186., 174., 272., 235., 135., 178.,\n",
       "       191., 159., 172., 184., 181., 236., 275., 154., 222., 214., 145.,\n",
       "       203., 231., 208., 226., 210., 197., 171., 155., 278., 189., 142.,\n",
       "       218., 179., 166., 237., 212., 177., 167., 132., 151., 216., 219.,\n",
       "       157., 224., 188., 161., 207., 156., 152., 220., 266., 228., 185.,\n",
       "       209., 204., 182., 200., 258., 146., 183., 163., 238., 194., 134.,\n",
       "       206., 136., 130., 190., 158., 147., 140., 265., 211., 138., 247.,\n",
       "       288., 234., 243., 256., 195., 213., 187., 205., 262., 285., 215.,\n",
       "       150., 139., 267., 149., 193., 230., 254., 269., 264., 199., 192.,\n",
       "       144., 131., 246., 287., 240., 263.])"
      ]
     },
     "execution_count": 3920,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_variance\"].unique()\n",
    "#Replacement of nan is successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3921,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1297ed0>"
      ]
     },
     "execution_count": 3921,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['scaled_variance'],rug=True)\n",
    "#Variable has multiple modes and concetrated between value range 135-280 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3922,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1412cd0>"
      ]
     },
     "execution_count": 3922,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAdQklEQVR4nO3de5RdZZnn8e8vSSGBgDRUCJBKLJpKnIZuFCgxLU0j0YBJqxlbR9EWq22bOBAJON4VERDW8ta4qNALEwRJZmiRS0YYh5gEFGycJpiEXEiipFpLKRJzQbnkAuTyzB97l5xdqVTVDrVr16nz+6x11tnvuy/1VM5KPefd77vfVxGBmZlZp2FlB2BmZoOLE4OZmWU4MZiZWYYTg5mZZTgxmJlZxoiyA3i16uvro7GxsewwzMyqyvLly7dFxOju9lV9YmhsbGTZsmVlh2FmVlUk/fZA+3wryczMMpwYzMwsw4nBzMwynBjMzCzDicHMrItt27Zx6aWX8swzz5QdSimcGMzMupgzZw6rVq1izpw5ZYdSCicGM7MK27ZtY8mSJQAsXry4JlsNTgxmZhXmzJnDvn37ANi3b19NthqcGMzMKjzwwAOZcmfroZY4MZiZVZDUY7kWODGYmVV4wxvekCmfdtppJUVSHicGM7MKv/zlLzPldevWlRRJeZwYzMwq7Nixo8dyLXBiMDOrMGrUqB7LtcCJwcyswlVXXZUpf/WrXy0nkBIVmhgkjZP0U0nrJa2VdFla/0ZJj0paKWmZpDPTeklqldQmabWk04uMz8ysqzPPPDNTPuOMM0qKpDxFtxj2AJ+KiL8AJgEzJZ0MfAO4OiLeCFyZlgGmAhPS1wzgpoLjMzPLeOyxxzLl5cuXlxRJeQpNDBGxKSJWpNsvAOuBsUAAR6aHvRbYmG5PB+ZH4lHgKEnHFxmjmVmlrreSvvzlL5cTSIkGbGlPSY3AacBS4HJgkaRvkSSnt6SHjQWeqjitI63b1OVaM0haFIwfP77IsM2sxmzfvr3Hci0YkM5nSaOAe4DLI+J54GLgkxExDvgkcEvnod2cHvtVRMyNiOaIaB49utu1rM3MDsrhhx/eY7kWFJ4YJNWRJIXbI2JBWt0CdG7fBXT29nQA4ypOb+CV20xmZoU79dRTM+WuT0LXgqJHJYmkNbA+Iq6v2LUROCfdngxsSLfvAz6Sjk6aBDwXEZnbSGZmRVq1alWmvHLlypIiKU/RfQxnARcCayR1/ut+EbgIuEHSCOBF0v4C4H5gGtAG7AQ+WnB8ZmYZU6ZM4d577/1T+bzzzisxmnIUmhgi4hG67zcA2G9wcEQEMLPImMzMenL22WdnEsM555zTw9FDk598HsRqfd1ZszLceOONmfINN9xQUiTlcWIYxObNm8fq1auZN29e2aGY1Yz29vYey7XAiWGQ2rZtGwsXLiQiWLhwoVsNZgOkoaGhx3ItcGIYpObNm0fS5ZKsO+tWg9nAaGpqypQnTJhQUiTlcWIYpJYsWcLu3bsB2L17N4sXLy45IrPa0HWupKVLl5YUSXmcGAapKVOmUFdXB0BdXV1NDpkzK8OUKVMy5Vr8v+fEMEi1tLT8aRHyYcOG0dLSUnJEZrXh7LPPzpQ9XNUGjfr6eqZOnYokpk6dyjHHHFN2SGY1wcNVB3B2VcuvpaWF9vZ2txbMBpCHq7rFMKjV19cze/ZstxbMBtAJJ5zQY7kWODGYmVXYu3dvj+Va4MRgZlZh8+bNPZZrgRODmZllODGYmVmGE4OZWYVhw4b1WK4Ftfcbm5n1YMyYMZnycccdV1Ik5Sl6ac9xkn4qab2ktZIuq9h3qaRfpfXfqKj/gqS2dN/5RcZnZtbVli1bMuVa7Hwu+gG3PcCnImKFpCOA5ZKWAGOA6cCpEfGSpGMBJJ0MXACcApwAPCBpYkTU3ngxM7OSFNpiiIhNEbEi3X4BWA+MBS4GvhYRL6X7OlP0dOCOiHgpIn5DsvbzmUXGaGZW6e1vf3um3HVSvVowYH0MkhqB04ClwETgbElLJT0s6U3pYWOBpypO60jrul5rhqRlkpZt3bq12MDNrKZ8/OMf77FcCwYkMUgaBdwDXB4Rz5PcwvozYBLwGeBOJVOJqpvTY7+KiLkR0RwRzaNHjy4wcjOrZZ0zHNeawhODpDqSpHB7RCxIqzuABZF4DNgH1Kf14ypObwA2Fh2jmVmnytUSI6ImV08selSSgFuA9RFxfcWuHwKT02MmAocA24D7gAskvUbSicAEILuckplZgbqulrho0aKSIilP0aOSzgIuBNZIWpnWfRG4FbhV0hPAy0BLJAscr5V0J7COZETTTI9IMrOBNGbMmMxU212fa6gFhSaGiHiE7vsNAD58gHOuA64rLCgzsx54Ej0/+WxmlnHeeef9qdNZEuefX3vP2XoFNzOrWq2trbS1tfXrNXfv3k1yZzuxYcMGZs2a1W/Xb2pq6tfrFcEtBjOzCnV1dYwYkXxnPvroo6mrqys5ooHnFoOZVa2ivnlffPHFtLe3893vfrcml9Z1i8HMrIu6ujomTJhQk0kBnBjMzKwLJwYzM8twYjAzswwnBjMzy3BiMDOzDCcGMzPLcGIwM7MMJwYzM8twYjAzswwnBjMzy3BiMDOzjKKX9hwn6aeS1ktaK+myLvs/LSkk1adlSWqV1CZptaTTi4zPzMz2V/TsqnuAT0XECklHAMslLYmIdZLGAVOA31UcP5VknecJwJuBm9J3s0IUMZ9/p46ODgAaGhr6/drVMKe/Va9CWwwRsSkiVqTbLwDrgbHp7m8DnwWi4pTpwPxIPAocJen4ImM0K8quXbvYtWtX2WGY5TZg6zFIagROA5ZKejfwdESs6lxCLzUWeKqi3JHWbepyrRnADIDx48cXF7QNeUV+6+68dmtra2E/w6wIA9L5LGkUcA9wOcntpS8BV3Z3aDd1sV9FxNyIaI6I5tGjR/drrGZmta7PiUHSGEm3SFqYlk+W9LE+nFdHkhRuj4gFwEnAicAqSe1AA7BC0nEkLYRxFac3ABv7GqOZmb16eVoMtwGLgBPS8pMkLYADUnKf6BZgfURcDxARayLi2IhojIhGkmRwekT8HrgP+Eg6OmkS8FxEbDrQ9c3MrP/lSQz1EXEnsA8gIvYAe3s55yzgQmCypJXpa1oPx98P/BpoA24GLskRn5mZ9YM8nc87JB1Des+/8xt9TydExCN0329QeUxjxXYAM3PEZGZm/SxPYvgfJLd6TpL0c2A08L5CojIzs9L0OTGkD6mdA7yepBXwq4jYXVhkZmZWijyjkmYCoyJibUQ8AYyS5D6AAm3bto1LL72UZ555puxQzKyG5Ol8viginu0sRMQfgYv6PyTrNG/ePFavXs28efPKDsXMakiexDBMFY8pSxoOHNL/IRkkrYWFCxcSEdx///1uNZjZgMmTGBYBd0p6m6TJwPeBHxcTls2bN4/du5MunN27d7vVYGYDJk9i+BzwE+BikiGlD5JMgmcFWLx4McnoXYgIFi1aVHJEZlYr8oxK2kcyDfZNxYVjncaMGUN7e3umbGY2EPqcGCSdBVwFvC49TyTPpP15MaHVts2bN/dYNjMrSp5bSbcA1wN/A7wJaE7frQDnnXcenX39kjj//PNLjsjMakWexPBcRCyMiC0R8Uznq7DIalxLSwsjRiQNurq6OlpaWkqOyMxqRZ7E8FNJ35T015JO73wVFlmNq6+vZ9q0aUhi2rRpHHPMMWWHZGY1Is9cSZ1rLzdX1AUwuf/CsUotLS20t7e7tWBmAyrPqKRziwzE9ldfX8/s2bPLDsPMakyuNZ8l/R1wCnBoZ11EXNPfQZmZWXnyDFf9DnAYcC7wXZIptx8rKC6zjNbWVtra2soOI5cNGzYAMGvWrJIj6bumpqaqiteKkafF8JaIOFXS6oi4WtK/AAt6OkHSOGA+cBzJym9zI+IGSd8E3gW8DPwn8NHOCfokfQH4GMnqcLMiwo/8Gm1tbTz5xArGj+pt0cDB45DdydiOF9t/UXIkffO77cPLDsEGiTyJYVf6vlPSCcAzwIm9nLMH+FS6lsMRwHJJS4AlwBciYo+krwNfAD4n6WTgApLbVScAD0iaGBHV89fACjN+1F6uaN5edhhD1rXLRpUdgg0SeYar/kjSUcA3gRVAO3BHTydExKaIWJFuvwCsB8ZGxOJ0zWiAR4GGdHs6cEdEvBQRvyFZ+/nMHDGamdmrlGdU0lfTzXsk/Qg4NCJ6XPO5kqRG4DRgaZdd/wT8IN0eS5IoOnWkdV2vNQOYATB+/Pi+hmBmZn3Qa2KQNDkifiLp77vZR0T02M+QHjcKuAe4PCKer6j/Esntpts7q7o5PfariJgLzAVobm7eb7+ZmR28vrQYziGZbvtd3ewLeu+AriNJCrdXJhFJLcA7gbdF5/zSSQthXMXpDcDGPsRoZmb9pNfEEBFfkTQMWBgRd+a5eLri2y3A+oi4vqL+HSTrO5wTETsrTrkP+DdJ15N0Pk/AQ2LNzAZUnzqf07UYPnEQ1z8LuBCYLGll+poG3AgcASxJ676T/py1wJ3AOpLV4WZ6RJKZ2cDKM1x1iaRPk3QU7+isjIg/HOiEiHiE7vsN7u/hnOuA63LEZWZm/ShPYvin9H1mRV0AXqjHzGwIyTNctbeH2czMbAjIO4neXwInk51Eb35/B2VmZuXJM4neV4C3kiSG+4GpwCMkcyGZmdkQkWdKjPcBbwN+HxEfBd4AvKaQqMzMrDR5EsOudNjqHklHAltwx7OZ2ZCTp49hWTqJ3s3AcmA7fvjMzGzIyTMq6ZJ08zuSfgwcGRGriwnLzMzK0udbSZLulfQhSYdHRLuTgpnZ0JSnj+F64G+AdZLukvQ+SYf2dpKZmVWXPLeSHgYeljQcmAxcBNwKHFlQbGZmVoK8D7iNJJl++wPA6cC8IoIy66qjo4MdLwz38pMF+u0Lwzm8o6PsMGwQyPOA2w+AN5PMevqvwEPp8FUzMxtC8rQYvgd86EDTYEuaEhFL+icss6yGhgZe3LOJK5q3lx3KkHXtslEc2tDQ+4E25OXpY/hxL4d8HXBiMLOM1tZW2trayg4jlw0bNgAwa9askiPJp6mpqV9iztXH0Ivu1l2wV+HJJ5/ksssuY/bs2TQ1NZUdjtlBaWtr4/G1j8NRZUeSQ3qT/PGnHy83jjye7b9L9WdiiK4VksaRTLJ3HMk/9dyIuEHS0SQL/jQC7cD7I+KP6VKgNwDTgJ3AP0bEin6Msapce+217Nixg2uuuYb58z1XoVWxo2DfW90lWaRhD+V5+qCXa/Xblbq3B/hURPwFMAmYKelk4PPAgxExAXgwLUMyY+uE9DUDuKng+AatJ598kvb2dgDa29urriluZtWrPxNDe9eKiNjU+Y0/Il4A1gNjgem8MtR1HvBf0+3pwPxIPAocJen4foyxalx77bWZ8jXXXFNSJGZWa3q9lSTp73vaHxEL0vcej5PUCJwGLAXGRMSm9LxNko5NDxsLPFVxWkdat6nLtWaQtCgYP358b79CVepsLRyobGZWlL70MbwrfT8WeAvwk7R8LvAQsKC3C0gaBdwDXB4RzyddCd0f2k3dfn0XETEXmAvQ3Ny83/6hoLGxMZMMGhsbS4vFzGpLr7eSIuKj6cI8AZwcEe+NiPcCp/TlB0iqI0kKt3e2LoDNnbeI0vctaX0HMK7i9AZgY59+kyHmiiuuyJSvvPLKkiIxs1qTp4+hsfP2T2ozMLGnE9JRRrcA6yPi+opd9wEt6XYLcG9F/UeUmAQ81+Vn1oyJEyf+qZXQ2Njo4apmNmDyJIaHJC2S9I+SWoD/C/y0l3POAi4EJktamb6mAV8DpkjaAExJy5CsJf1roI1kQaBLurlmzbjiiis4/PDD3VowswGV58nnT0h6D/C3adXciPjfvZzzCAd+8O1t3RwfwMy+xjTUTZw4kYULF5YdhpnVmLwPuK0AXoiIByQdJumIdBiqmZkNEXlWcLsIuBuYk1aNBX5YRFBmZlaePH0MM0n6DJ4HiIgNJENYzcxsCMmTGF6KiJc7C5JG0M0zBmZmVt3yJIaHJX0RGClpCnAX8H+KCcvMzMqSJzF8HtgKrAE+TjK09IoezzAzs6qTZ7jqPpJnC24uLhwzMytbXybRW0MPfQkRcWq/RmRmZqXqS4vhnYVHYWZmg0aviSEifjsQgZiZ2eCQ5wG3SZJ+IWm7pJcl7ZX0fJHBmZnZwMszKulG4IPABmAk8M/A7CKCMjOz8uSaKyki2iQNj4i9wPck/b+C4jIzs5LkSQw7JR0CrJT0DZLlNg8vJiwzMytLnltJF6bHfwLYQbLS2nuLCMrMzMqTp8WwDXg5Il4ErpY0HHhNMWGZmVlZ8rQYHgQOqyiPBB7o6QRJt0raIumJiro3Sno0Xc1tmaQz03pJapXUJmm1pNPz/CJmZtY/8iSGQyNie2ch3T6sh+MBbgPe0aXuG8DVEfFG4Mq0DDAVmJC+ZgA35YjNzMz6SZ5bSTsknR4RKwAkNQO7ejohIn4mqbFrNXBkuv1aYGO6PR2Yny7v+aikoyQdHxGbcsRYitbWVtra2vr9uh0dHQA0NDT0+7WbmpqYNWtWv1/XzKpfnsRwGXCXpI0kf9xPAD5wED/zcmCRpG+RtFjektaPBZ6qOK4jrdsvMUiaQdKqYPz48QcRQnXYtavHvGtmVog8ieFE4DRgPPAeYBIHt1DPxcAnI+IeSe8HbgHeDqibY7u9fkTMBeYCNDc3l75YUFHfvDuv29raWsj1zcy6k6eP4csR8TxwFDCF5A/zwfQDtAAL0u27gDPT7Q6SIbCdGnjlNpOZmQ2QPC2Gven73wHfiYh7JV11ED9zI3AO8BAwmWSKDYD7gE9IugN4M/BcNfQvmFnPOjo64DkY9lCe76GW27PQER39cqk8ieFpSXNIbvt8XdJr6KXFIen7wFuBekkdwFeAi4Ab0jWjXyTtKyBZEW4a0AbsBD6aIzYzM+sneRLD+0mGnn4rIp6VdDzwmZ5OiIgPHmDXGd0cG8DMHPGYWRVoaGhgq7ay7637yg5lSBv20DAaxvbPCMY8S3vu5JW+AdLbPL7VY2Y2xOSaXdWsTL/bPpxrl40qO4w+27wzudM65rDq+Kb8u+3DmVh2EDYoODFYVWhqaio7hNxe3pCMqzi0cULJkfTNRKrz39n6X80khqKeTi7ShvQPS7U9oVzEU9XV9m8Afg7FqlfNJIa2tjYeX7OOfYcdXXYofaaXk2f3lv/n70uOpO+G7fxD2SGY2atUM4kBYN9hR/Piye8sO4wh7dB1Pyo7BDN7lWoqMZhZSZ6tsgfcOueRrp6xDvAsyexy/cCJwcwKVY0d2p39exPGVsfAAQDG9t+/tRODmRXKAweqTxW17czMbCA4MZiZWYYTg5mZZTgxmJlZRs10Pnd0dDBs53MeZ1+wYTufoaNjT9lhmNmr4BaDmZll1EyLoaGhgc0vjfCTzwU7dN2PaGg4ruwwrEYUNQdakfOUFTGXWH8rtMUg6VZJWyQ90aX+Ukm/krRW0jcq6r8gqS3dd36RsZmZHcjIkSMZOXJk2WGUpugWw23AjcD8zgpJ5wLTgVMj4iVJx6b1JwMXAKcAJwAPSJoYEXv3u6qZGdX58Fw1KLTFEBE/A7pOt3kx8LWIeCk9ZktaPx24IyJeiojfkKz9fGaR8ZmZ2f7K6HyeCJwtaamkhyW9Ka0fCzxVcVwHB5gSStIMScskLdu6dWvB4ZqZ1ZYyEsMI4M+AScBngDslCVA3x0Z3F4iIuRHRHBHNo0ePLi5SM7MaVEZi6AAWROIxYB9Qn9aPqziuAdhYQnxmZjWtjMTwQ2AygKSJwCHANuA+4AJJr5F0IjABeKyE+MzMalqho5IkfR94K1AvqQP4CnArcGs6hPVloCUiAlgr6U5gHbAHmNnfI5KG7fxDVT35rBefByAOPbLkSPouWdrTzzGYVbNCE0NEfPAAuz58gOOvA64rIpbqXCzkBQAmnFRNf2iPq8p/azN7Rc08+VyN451rfbEQMyuH50oyM7MMJwYzM8twYjAzswwnBjMzy3BiMDOzDCcGMzPLcGIwM7MMJwYzM8twYjAzswwnBjMzy3BiMDOzDCcGMzPLcGIwM7MMJwYzM8twYjAzs4xCE4OkWyVtSVdr67rv05JCUn1alqRWSW2SVks6vcjYzMyse0W3GG4D3tG1UtI4YArwu4rqqSTrPE8AZgA3FRybmZl1o9DEEBE/A/7Qza5vA58FoqJuOjA/Eo8CR0k6vsj4zMxsfwPexyDp3cDTEbGqy66xwFMV5Y60rrtrzJC0TNKyrVu3FhSpmVltGtDEIOkw4EvAld3t7qYuuqkjIuZGRHNENI8ePbo/QzQzq3kjBvjnnQScCKySBNAArJB0JkkLYVzFsQ3AxgGOz8ys5g1oiyEi1kTEsRHRGBGNJMng9Ij4PXAf8JF0dNIk4LmI2DSQ8ZmZWfHDVb8P/Afwekkdkj7Ww+H3A78G2oCbgUuKjM3MzLpX6K2kiPhgL/sbK7YDmFlkPGZm1js/+WxmZhkD3fk8JLW2ttLW1tbv192wYQMAs2bN6vdrNzU1FXJdM6t+TgyD2MiRI8sOwcxqkBNDP/A3bzMbStzHYGZmGU4MZmaW4VtJVtOKGjgAHjxg1cuJwawgHjxg1cqJwWqav3Wb7c99DGZmluHEYGZmGU4MZmaW4cRgZmYZTgxmZpbhxGBmZhlODGZmluHEYGZmGUoWTqtekrYCvy07jgLVA9vKDsIOmj+/6jXUP7vXRcTo7nZUfWIY6iQti4jmsuOwg+PPr3rV8mfnW0lmZpbhxGBmZhlODIPf3LIDsFfFn1/1qtnPzn0MZmaW4RaDmZllODGYmVmGE4OZ1SRJjZKeKDuOwciJwawEkrx6og1aTgwlkvR1SZdUlK+S9BVJD0paIWmNpOnpvkZJ6yXdLGmtpMWSvKjwICDpI5JWS1ol6X9KepekpZIel/SApDHpcVdJmitpMTC/5LAtMULSvPTzu1vSYZLaJdUDSGqW9FC6fY6klenrcUlHlBp5gZwYynUH8IGK8vuB7wHviYjTgXOBf5GkdP8E4F8j4hTgWeC9Axms7U/SKcCXgMkR8QbgMuARYFJEnEbyGX+24pQzgOkR8aEBD9a683pgbkScCjwPXNLDsZ8GZkbEG4GzgV0DEF8p3JwtUUQ8LulYSScAo4E/ApuAb0v6W2AfMBYYk57ym4hYmW4vBxoHOGTb32Tg7ojYBhARf5D0V8APJB0PHAL8puL4+yJiyP5BqUJPRcTP0+3/Bczq4difA9dLuh1YEBEdhUdXErcYync38D6SlsMdwD+QJIkz0m8mm4FD02NfqjhvL07sg4GArg8DzQZujIi/Aj7OK58fwI6BCsz6pOtnF8AeXvnb+KfPLiK+BvwzMBJ4VNJ/GZAIS+DEUL47gAtIksPdwGuBLRGxW9K5wOvKDM569SDwfknHAEg6muQzfDrd31JWYNYn4yX9dbr9QZLbgO0kt/yg4natpJMiYk1EfB1YBjgxWDEiYi1wBPB0RGwCbgeaJS0jaT38ssz4rGfp53cd8LCkVcD1wFXAXZL+naE9bfNQsB5okbQaOBq4CbgauCH9/PZWHHu5pCfSz3kXsHDAox0gnhLDzMwy3GIwM7MMJwYzM8twYjAzswwnBjMzy3BiMDOzDCcGs1cpnQPp02XHYdZfnBjMzCzDicEsp66zqXbZd5GkX6T77pF0WFr/3zofjpL0s7TuFEmPpbN1rpY0oYzfx6wrP+BmlkM6m+oC4KyI2JZOgTEL2B4R35J0TEQ8kx57LbA5ImZLWgO8IyKelnRURDwraTbwaETcLukQYLgn2LPBwC0Gs3z2m021y/6/lPTvaSL4B+CUtP7nwG2SLgKGp3X/AXxR0ueA1zkp2GDhxGCWT3ezqVa6DfhEOrPq1aSzc0bEfweuAMYBK9OWxb8B7yaZd2eRpMlFBm7WV04MZvl0N5tqpSOATZLqSFoMpMedFBFLI+JKkon1xkn6c+DXEdEK3AecOiC/gVkvPJ+/WQ4RsVZS52yqe4HHSaZp7vRlYCnwW2ANSaIA+GbauSyS5LIK+DzwYUm7gd8D1wzIL2HWC3c+m5lZhm8lmZlZhhODmZllODGYmVmGE4OZmWU4MZiZWYYTg5mZZTgxmJlZxv8HoEvBrGHrXHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-scaled_variance vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['scaled_variance'])\n",
    "#Inference: From below graph, it is evident that scaled_variance varies for each class, van being lowest one\n",
    "#Car has wide spread range w.r.t scaled_variance\n",
    "#Bus and van has outliers w.r.t scaled_variance, bus has right skewed distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3923,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 379.,  330.,  635.,  309.,  325.,  957.,  361.,  281.,  223.,\n",
       "        505.,  266.,  224.,  349.,  345.,  465.,  624.,  206.,  485.,\n",
       "        686.,  651.,  354.,  221.,  344.,  307.,  623.,  324.,  238.,\n",
       "        696.,  570.,  314.,  356.,  293.,  304.,  641.,  402.,  363.,\n",
       "        340.,  346.,  691.,  336.,  628.,  207.,  366.,  405.,  675.,\n",
       "        371.,  253.,  317.,  352.,  404.,  299.,  355.,  661.,  341.,\n",
       "        956.,  265.,  512.,  653.,  241.,  567.,  247.,  269.,  333.,\n",
       "        523.,  323.,  748.,  305.,  558.,  683.,  732.,  466.,  227.,\n",
       "        338.,  571.,  445.,  666.,  328.,  343.,  671.,  242.,  311.,\n",
       "        342.,  998.,  209.,  446.,  229.,  703.,  430.,  583.,  312.,\n",
       "        308.,  337.,  602.,  321.,  326.,  347.,  246.,  194.,  576.,\n",
       "        711.,  575.,  331.,  329.,  524.,  357.,  315.,  192.,  351.,\n",
       "        611.,  712.,  463.,  370.,  319.,  216.,  365.,  605.,  578.,\n",
       "        511.,  261.,  669.,  364.,  264.,  230.,  373.,  320.,  670.,\n",
       "        406.,  728.,  387.,  332.,  360.,  279.,  527.,  525.,  334.,\n",
       "        645.,  928.,  240.,  335.,  259.,  610.,  415.,  260.,  665.,\n",
       "        707.,  674.,  243.,  892.,  313.,  680.,  469.,  273.,  638.,\n",
       "        196.,  612.,  479.,  434.,  367.,  494.,  866.,  705.,  350.,\n",
       "        727.,  225.,  396.,  681.,  444.,  284.,  682.,  286.,  388.,\n",
       "        708.,  195.,  722.,  531.,  274.,  704.,  258.,  640.,  203.,\n",
       "        184.,  642.,  268.,  419.,  731.,  684.,  358.,  374.,  426.,\n",
       "        673.,  197.,  716.,  310.,  676.,  604.,  245.,  457.,  237.,\n",
       "        518.,  212.,  218.,  322.,  452.,  316.,  954.,  327.,  362.,\n",
       "        521.,  692.,  427.,  598.,  251.,  870.,  252.,  385.,   nan,\n",
       "        492.,  589.,  249.,  520.,  213.,  718.,  982.,  533.,  435.,\n",
       "        389.,  300.,  530.,  725.,  822.,  720.,  625.,  290.,  271.,\n",
       "        737.,  833.,  668.,  546.,  472.,  504.,  519.,  687.,  425.,\n",
       "        608.,  399.,  359.,  429.,  205.,  776.,  694.,  923.,  296.,\n",
       "        756.,  348.,  467.,  391.,  966.,  413.,  627.,  294.,  595.,\n",
       "        433.,  390.,  287.,  685.,  263.,  255.,  650.,  298.,  710.,\n",
       "        428.,  639.,  280.,  629.,  262.,  473.,  586.,  572.,  376.,\n",
       "        211.,  517.,  844.,  636.,  394.,  607.,  282.,  706.,  480.,\n",
       "        735.,  658.,  741.,  697.,  418.,  301.,  383.,  663.,  709.,\n",
       "        891.,  204.,  757.,  573.,  667.,  372.,  678.,  395.,  729.,\n",
       "        278.,  700.,  339.,  471.,  275.,  291.,  701.,  381.,  526.,\n",
       "        630.,  460.,  904.,  637.,  719.,  601.,  857.,  375.,  378.,\n",
       "        277.,  208.,  295.,  458.,  613.,  855.,  698.,  713.,  416.,\n",
       "        688.,  648.,  369.,  677.,  600.,  409.,  232.,  622.,  200.,\n",
       "        250.,  481.,  534.,  422.,  462.,  191.,  393.,  193.,  219.,\n",
       "        486.,  664.,  306.,  644.,  616.,  297.,  487.,  562.,  543.,\n",
       "        484.,  318.,  440.,  289.,  561.,  513.,  659.,  455.,  596.,\n",
       "        353.,  752.,  579.,  414.,  987.,  220.,  545.,  489.,  417.,\n",
       "        693.,  438.,  657.,  222.,  474.,  766.,  587.,  459.,  408.,\n",
       "        557.,  256.,  233.,  633.,  730.,  816.,  968.,  621.,  584.,\n",
       "        506.,  401.,  559.,  272.,  660.,  574.,  536.,  450.,  552.,\n",
       "        838.,  535.,  285.,  631.,  563.,  476.,  508.,  597.,  717.,\n",
       "        478.,  726.,  254.,  382., 1018.,  283.,  368.,  721.,  270.])"
      ]
     },
     "execution_count": 3923,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysis of variable-scaled_variance.1\n",
    "#Previous analysis indicated that nan values are present\n",
    "vehicles['scaled_variance.1'].unique()\n",
    "#nan is shwon as one of the unique value in data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3924,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1528090>"
      ]
     },
     "execution_count": 3924,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking for outliers\n",
    "sns.boxplot(vehicles['scaled_variance.1'])\n",
    "#outliers present. We can consider values >990 as outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3925,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "363.5"
      ]
     },
     "execution_count": 3925,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing outliers with median\n",
    "vehicles['scaled_variance.1'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3926,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"scaled_variance.1\"]=np.where(vehicles[\"scaled_variance.1\"]>990, 363.5,vehicles[\"scaled_variance.1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3927,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"scaled_variance.1\"].fillna((vehicles[\"scaled_variance.1\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3928,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([379.  , 330.  , 635.  , 309.  , 325.  , 957.  , 361.  , 281.  ,\n",
       "       223.  , 505.  , 266.  , 224.  , 349.  , 345.  , 465.  , 624.  ,\n",
       "       206.  , 485.  , 686.  , 651.  , 354.  , 221.  , 344.  , 307.  ,\n",
       "       623.  , 324.  , 238.  , 696.  , 570.  , 314.  , 356.  , 293.  ,\n",
       "       304.  , 641.  , 402.  , 363.  , 340.  , 346.  , 691.  , 336.  ,\n",
       "       628.  , 207.  , 366.  , 405.  , 675.  , 371.  , 253.  , 317.  ,\n",
       "       352.  , 404.  , 299.  , 355.  , 661.  , 341.  , 956.  , 265.  ,\n",
       "       512.  , 653.  , 241.  , 567.  , 247.  , 269.  , 333.  , 523.  ,\n",
       "       323.  , 748.  , 305.  , 558.  , 683.  , 732.  , 466.  , 227.  ,\n",
       "       338.  , 571.  , 445.  , 666.  , 328.  , 343.  , 671.  , 242.  ,\n",
       "       311.  , 342.  , 363.5 , 209.  , 446.  , 229.  , 703.  , 430.  ,\n",
       "       583.  , 312.  , 308.  , 337.  , 602.  , 321.  , 326.  , 347.  ,\n",
       "       246.  , 194.  , 576.  , 711.  , 575.  , 331.  , 329.  , 524.  ,\n",
       "       357.  , 315.  , 192.  , 351.  , 611.  , 712.  , 463.  , 370.  ,\n",
       "       319.  , 216.  , 365.  , 605.  , 578.  , 511.  , 261.  , 669.  ,\n",
       "       364.  , 264.  , 230.  , 373.  , 320.  , 670.  , 406.  , 728.  ,\n",
       "       387.  , 332.  , 360.  , 279.  , 527.  , 525.  , 334.  , 645.  ,\n",
       "       928.  , 240.  , 335.  , 259.  , 610.  , 415.  , 260.  , 665.  ,\n",
       "       707.  , 674.  , 243.  , 892.  , 313.  , 680.  , 469.  , 273.  ,\n",
       "       638.  , 196.  , 612.  , 479.  , 434.  , 367.  , 494.  , 866.  ,\n",
       "       705.  , 350.  , 727.  , 225.  , 396.  , 681.  , 444.  , 284.  ,\n",
       "       682.  , 286.  , 388.  , 708.  , 195.  , 722.  , 531.  , 274.  ,\n",
       "       704.  , 258.  , 640.  , 203.  , 184.  , 642.  , 268.  , 419.  ,\n",
       "       731.  , 684.  , 358.  , 374.  , 426.  , 673.  , 197.  , 716.  ,\n",
       "       310.  , 676.  , 604.  , 245.  , 457.  , 237.  , 518.  , 212.  ,\n",
       "       218.  , 322.  , 452.  , 316.  , 954.  , 327.  , 362.  , 521.  ,\n",
       "       692.  , 427.  , 598.  , 251.  , 870.  , 252.  , 385.  , 363.25,\n",
       "       492.  , 589.  , 249.  , 520.  , 213.  , 718.  , 982.  , 533.  ,\n",
       "       435.  , 389.  , 300.  , 530.  , 725.  , 822.  , 720.  , 625.  ,\n",
       "       290.  , 271.  , 737.  , 833.  , 668.  , 546.  , 472.  , 504.  ,\n",
       "       519.  , 687.  , 425.  , 608.  , 399.  , 359.  , 429.  , 205.  ,\n",
       "       776.  , 694.  , 923.  , 296.  , 756.  , 348.  , 467.  , 391.  ,\n",
       "       966.  , 413.  , 627.  , 294.  , 595.  , 433.  , 390.  , 287.  ,\n",
       "       685.  , 263.  , 255.  , 650.  , 298.  , 710.  , 428.  , 639.  ,\n",
       "       280.  , 629.  , 262.  , 473.  , 586.  , 572.  , 376.  , 211.  ,\n",
       "       517.  , 844.  , 636.  , 394.  , 607.  , 282.  , 706.  , 480.  ,\n",
       "       735.  , 658.  , 741.  , 697.  , 418.  , 301.  , 383.  , 663.  ,\n",
       "       709.  , 891.  , 204.  , 757.  , 573.  , 667.  , 372.  , 678.  ,\n",
       "       395.  , 729.  , 278.  , 700.  , 339.  , 471.  , 275.  , 291.  ,\n",
       "       701.  , 381.  , 526.  , 630.  , 460.  , 904.  , 637.  , 719.  ,\n",
       "       601.  , 857.  , 375.  , 378.  , 277.  , 208.  , 295.  , 458.  ,\n",
       "       613.  , 855.  , 698.  , 713.  , 416.  , 688.  , 648.  , 369.  ,\n",
       "       677.  , 600.  , 409.  , 232.  , 622.  , 200.  , 250.  , 481.  ,\n",
       "       534.  , 422.  , 462.  , 191.  , 393.  , 193.  , 219.  , 486.  ,\n",
       "       664.  , 306.  , 644.  , 616.  , 297.  , 487.  , 562.  , 543.  ,\n",
       "       484.  , 318.  , 440.  , 289.  , 561.  , 513.  , 659.  , 455.  ,\n",
       "       596.  , 353.  , 752.  , 579.  , 414.  , 987.  , 220.  , 545.  ,\n",
       "       489.  , 417.  , 693.  , 438.  , 657.  , 222.  , 474.  , 766.  ,\n",
       "       587.  , 459.  , 408.  , 557.  , 256.  , 233.  , 633.  , 730.  ,\n",
       "       816.  , 968.  , 621.  , 584.  , 506.  , 401.  , 559.  , 272.  ,\n",
       "       660.  , 574.  , 536.  , 450.  , 552.  , 838.  , 535.  , 285.  ,\n",
       "       631.  , 563.  , 476.  , 508.  , 597.  , 717.  , 478.  , 726.  ,\n",
       "       254.  , 382.  , 283.  , 368.  , 721.  , 270.  ])"
      ]
     },
     "execution_count": 3928,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_variance.1\"].unique()\n",
    "#Replacement of nan is successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3929,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1600710>"
      ]
     },
     "execution_count": 3929,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['scaled_variance.1'], rug=True)\n",
    "#Variable has multiple modes and concetrated between value range 200-800 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3930,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1768050>"
      ]
     },
     "execution_count": 3930,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-scaled_variance.1 vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['scaled_variance.1'])\n",
    "#Inference: From below graph, it is evident that scaled_variance.1 varies for each class, van being lowest one\n",
    "#Car has wide spread range w.r.t scaled_variance.1\n",
    "#Bus and van has outliers w.r.t scaled_variance.1, bus has right skewed distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3931,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variables-scaled_variance and scaled_variance.1 displays almost similar relationship with taregt variable 'class'. \n",
    "#I think once of them can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3932,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-scaled_radius_of_gyration\n",
    "#This variable showed presence of nan values previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3933,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([184., 158., 220., 127., 188., 264., 172., 164., 112., 152., 118.,\n",
       "       192., 161., 206., 246., 125., 151., 223., 133., 177., 141., 224.,\n",
       "       174., 139., 216., 163., 120., 204., 130., 200., 218., 186., 202.,\n",
       "       232., 189., 156., 179., 146., 245., 165., 230., 119., 176., 212.,\n",
       "       142., 185., 159., 183., 171., 209., 221., 147., 210., 173., 214.,\n",
       "       116., 144., 148., 257., 162., 129., 132., 229., 155., 234., 170.,\n",
       "       201., 150., 137., 167., 143., 187., 136., 138., 194., 153., 157.,\n",
       "       198., 131., 168., 134., 145., 115., 178., 199., 195., 154., 149.,\n",
       "       239., 140., 180., 217., 124., 190., 242., 169., 135., 166., 238.,\n",
       "       121., 128., 191., 123., 219., 181.,  nan, 213., 175., 197., 211.,\n",
       "       126., 249., 203., 205., 160., 222., 247., 226., 261., 231., 236.,\n",
       "       253., 235., 182., 262., 193., 117., 250., 196., 243., 113., 228.,\n",
       "       240., 241., 244., 109., 207., 268., 260., 215., 208., 237., 255.,\n",
       "       114.])"
      ]
     },
     "execution_count": 3933,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_radius_of_gyration\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'scaled_radius_of_gyration' variable has any outliers \n",
    "#as this will impact the replacement value of nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3934,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1885e10>"
      ]
     },
     "execution_count": 3934,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['scaled_radius_of_gyration'])\n",
    "#Boxplot does not show any presence of outliers for scaled_radius_of_gyration\n",
    "#So nan value can be replaced with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3935,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with median\n",
    "vehicles[\"scaled_radius_of_gyration\"].fillna((vehicles[\"scaled_radius_of_gyration\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3936,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([184. , 158. , 220. , 127. , 188. , 264. , 172. , 164. , 112. ,\n",
       "       152. , 118. , 192. , 161. , 206. , 246. , 125. , 151. , 223. ,\n",
       "       133. , 177. , 141. , 224. , 174. , 139. , 216. , 163. , 120. ,\n",
       "       204. , 130. , 200. , 218. , 186. , 202. , 232. , 189. , 156. ,\n",
       "       179. , 146. , 245. , 165. , 230. , 119. , 176. , 212. , 142. ,\n",
       "       185. , 159. , 183. , 171. , 209. , 221. , 147. , 210. , 173. ,\n",
       "       214. , 116. , 144. , 148. , 257. , 162. , 129. , 132. , 229. ,\n",
       "       155. , 234. , 170. , 201. , 150. , 137. , 167. , 143. , 187. ,\n",
       "       136. , 138. , 194. , 153. , 157. , 198. , 131. , 168. , 134. ,\n",
       "       145. , 115. , 178. , 199. , 195. , 154. , 149. , 239. , 140. ,\n",
       "       180. , 217. , 124. , 190. , 242. , 169. , 135. , 166. , 238. ,\n",
       "       121. , 128. , 191. , 123. , 219. , 181. , 173.5, 213. , 175. ,\n",
       "       197. , 211. , 126. , 249. , 203. , 205. , 160. , 222. , 247. ,\n",
       "       226. , 261. , 231. , 236. , 253. , 235. , 182. , 262. , 193. ,\n",
       "       117. , 250. , 196. , 243. , 113. , 228. , 240. , 241. , 244. ,\n",
       "       109. , 207. , 268. , 260. , 215. , 208. , 237. , 255. , 114. ])"
      ]
     },
     "execution_count": 3936,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_radius_of_gyration\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3937,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa196a110>"
      ]
     },
     "execution_count": 3937,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['scaled_radius_of_gyration'], rug=True)\n",
    "#\"scaled_radius_of_gyration\" variable has almost symmetrical distribution across the data set\n",
    "#Data concentration can be seen between value ranges-115-260 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3938,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1ad1e50>"
      ]
     },
     "execution_count": 3938,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-scaled_radius_of_gyration vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['scaled_radius_of_gyration'])\n",
    "#Inference: From below graph, it is evident that scaled_radius_of_gyration varies for each class\n",
    "#Car has wide spread range w.r.t scaled_radius_of_gyration\n",
    "#Bus has outliers w.r.t scaled_radius_of_gyration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3939,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-scaled_radius_of_gyration.1\n",
    "#This variable showed presence of nan values previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3940,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 70.,  72.,  73.,  63., 127.,  85.,  66.,  67.,  64.,  65.,  71.,\n",
       "        74.,  80.,  75.,  82.,  68.,  69.,  76.,  83., 118.,  86.,  77.,\n",
       "        88.,  62.,  79.,  78.,  nan,  81.,  87., 119.,  97.,  60.,  61.,\n",
       "        89.,  90.,  84., 135.,  91.,  59.,  99.])"
      ]
     },
     "execution_count": 3940,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_radius_of_gyration.1\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'scaled_radius_of_gyration.1' variable has any outliers \n",
    "#as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3941,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1bf3850>"
      ]
     },
     "execution_count": 3941,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['scaled_radius_of_gyration.1'])\n",
    "#Boxplot does shows the presence of outliers for scaled_radius_of_gyration.1\n",
    "#Values>88 can be considered as outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3942,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.5"
      ]
     },
     "execution_count": 3942,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_radius_of_gyration.1\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3943,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"scaled_radius_of_gyration.1\"]=np.where(vehicles[\"scaled_radius_of_gyration.1\"]>88, 71.5,vehicles[\"scaled_radius_of_gyration.1\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3944,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"scaled_radius_of_gyration.1\"].fillna((vehicles[\"scaled_radius_of_gyration.1\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3945,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70.  , 72.  , 73.  , 63.  , 71.5 , 85.  , 66.  , 67.  , 64.  ,\n",
       "       65.  , 71.  , 74.  , 80.  , 75.  , 82.  , 68.  , 69.  , 76.  ,\n",
       "       83.  , 86.  , 77.  , 88.  , 62.  , 79.  , 78.  , 71.25, 81.  ,\n",
       "       87.  , 60.  , 61.  , 84.  , 59.  ])"
      ]
     },
     "execution_count": 3945,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"scaled_radius_of_gyration.1\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3946,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1cdef10>"
      ]
     },
     "execution_count": 3946,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['scaled_radius_of_gyration.1'], rug=True)\n",
    "#\"scaled_radius_of_gyration.1\" variable has almost symmetrical distribution across the data set\n",
    "#Data concentration can be seen between value ranges-60-88 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3947,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1e67750>"
      ]
     },
     "execution_count": 3947,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-scaled_radius_of_gyration.1 vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['scaled_radius_of_gyration.1'])\n",
    "#Inference: From below graph, it is evident that scaled_radius_of_gyration.1 varies for each class\n",
    "#bus has wide spread range w.r.t scaled_radius_of_gyration.1\n",
    "#van and car has outliers w.r.t scaled_radius_of_gyration.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3948,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variables-scaled_radius_of_gyration and scaled_radius_of_gyration.1 does not display similar behavious\n",
    "#w.r.t target variable 'class', so think chances of dropping one of the column is less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3949,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-skewness_about\n",
    "#This variable showed presence of nan values previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3950,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.,  9., 14.,  5., 13.,  3.,  2.,  4.,  8.,  0.,  7.,  1., 10.,\n",
       "       17., 20., 18., nan, 11., 16., 21., 12., 22., 15., 19.])"
      ]
     },
     "execution_count": 3950,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'skewness_about' variable has any outliers \n",
    "#as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3951,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa1f6e190>"
      ]
     },
     "execution_count": 3951,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['skewness_about'])\n",
    "#Boxplot does shows the presence of outliers for skewness_about\n",
    "#Values >19 can be considered as outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3952,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 3952,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3953,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"skewness_about\"]=np.where(vehicles[\"skewness_about\"]>19, 6,vehicles[\"skewness_about\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3954,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"skewness_about\"].fillna((vehicles[\"skewness_about\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3955,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.,  9., 14.,  5., 13.,  3.,  2.,  4.,  8.,  0.,  7.,  1., 10.,\n",
       "       17., 18., 11., 16., 12., 15., 19.])"
      ]
     },
     "execution_count": 3955,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3956,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa204a190>"
      ]
     },
     "execution_count": 3956,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['skewness_about'], rug=True)\n",
    "#\"skewness_about\" variable has slightly right skewed  distribution across the data set with multiple modes\n",
    "#Data concentration can be seen between value ranges-0-19 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3957,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa21adf90>"
      ]
     },
     "execution_count": 3957,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-skewness_about vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['skewness_about'])\n",
    "#Inference: From below graph, it is evident that skewness_about is almost overlapping for each class\n",
    "#car has wide spread range w.r.t skewness_about\n",
    "#van and bus has outliers w.r.t skewness_about"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3958,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-skewness_about.1\n",
    "#This variable showed presence of nan values previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3959,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16., 14.,  9., 10., 11.,  1.,  3., 26., 13.,  2.,  5.,  6.,  4.,\n",
       "       28.,  7., 20., 38., 25., 15.,  0.,  8., 24., 21., 18., 23., 30.,\n",
       "       12., 29., 27., nan, 33., 32., 41., 39., 17., 22., 19., 35., 31.,\n",
       "       36., 40., 34.])"
      ]
     },
     "execution_count": 3959,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about.1\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'skewness_about.1' variable has any outliers \n",
    "#as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3960,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa22c6050>"
      ]
     },
     "execution_count": 3960,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['skewness_about.1'])\n",
    "#Boxplot does shows the presence of outliers for skewness_about.1\n",
    "#Values >40 can be considered as outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3961,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 3961,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about.1\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3962,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicles[\"skewness_about.1\"]=np.where(vehicles[\"skewness_about.1\"]>40, 11,vehicles[\"skewness_about.1\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3963,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"skewness_about.1\"].fillna((vehicles[\"skewness_about.1\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3964,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16., 14.,  9., 10., 11.,  1.,  3., 26., 13.,  2.,  5.,  6.,  4.,\n",
       "       28.,  7., 20., 38., 25., 15.,  0.,  8., 24., 21., 18., 23., 30.,\n",
       "       12., 29., 27., 33., 32., 39., 17., 22., 19., 35., 31., 36., 40.,\n",
       "       34.])"
      ]
     },
     "execution_count": 3964,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about.1\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3965,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa23a9290>"
      ]
     },
     "execution_count": 3965,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEICAYAAABWJCMKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3hc5ZX48e+ZGfVeXVQsyZZ7w51iOsHAggOYYEgIydJS2JBNdhMSfssSstmEZHfZbCAEEkiAhJgODhgcwIDBGDdcZbnItmw1qxfL6pr398dcEUVI1sga6U45n+fRo5l735k5rzyeM/etYoxBKaVU6HHYHYBSSil7aAJQSqkQpQlAKaVClCYApZQKUZoAlFIqRGkCUEqpEOXyppCILAN+CTiB3xljftbnfATwFDAfqAWuN8YUi0gOUAjst4p+bIz52qleKzU11eTk5AyhCkoppbZt21ZjjEkbymMGTQAi4gQeBi4BSoEtIrLaGLO3V7FbgHpjzCQRWQk8AFxvnTtkjJnrbUA5OTls3brV6woopZQCETk61Md40wS0CCgyxhw2xnQAq4DlfcosB560br8AXCQiMtRglFJKjR5vEkAGUNLrfql1rN8yxpguoBFIsc7lish2EXlfRJYOM16llFI+4k0fQH/f5PuuHzFQmQog2xhTKyLzgVdEZIYxpunvHixyO3A7QHZ2thchKaWUGi5vrgBKgaxe9zOB8oHKiIgLSADqjDHtxphaAGPMNuAQMLnvCxhjHjPGLDDGLEhLG1IfhlJKqdPkTQLYAuSLSK6IhAMrgdV9yqwGbrZurwDWGWOMiKRZnciISB6QDxz2TehKKaWGY9AmIGNMl4jcCazFMwz0CWNMgYjcD2w1xqwGHgeeFpEioA5PkgA4F7hfRLqAbuBrxpi6kaiIUkqpoRF/Ww56wYIFRoeBKqXU0IjINmPMgqE8RmcCK6VUiNIEoJRSIcqrpSCU6vHMpmPDevyNi3WYr1L+Qq8AlFIqRGkCUEqpEKUJQCmlQpQmAKWUClGaAJRSKkRpAlBKqRClw0BDzHCHcSqlgodeASilVIjSBKCUUiFKE4BSSoUoTQBKKRWiNAEopVSI0gSglFIhSoeBBhgdxqmU8hW9AlBKqRClCUAppUKUNgGFoJb2Lqqb26lv6eRkexcOh+AUISHKRXJMBMkx4TgdYneYSqkRpgkgRDS1dbK1uJ79x5sorW/FnKJsmFOYkBxDXloM08fFkx4fOWpxKqVGjyaAINfY2sn7B6rYWlxPt9uQkRTFhVPTyUyKJik6jNgIF26gq9tNY2sntSc7KKtv5UjNSf66t5K/7q1kbHwk87ITmT8hmahwp91VUkr5iCaAILaztIFXd5TR2WWYNyGRc/PTSImNGLB8YnQ4E1JimJedBEBTayd7yhvZWdLAmj3HeauwkvkTkjhvcjoJUWGjVQ2l1AjRBDDKRmMYZ1e3m5e3l7G9pIHs5Gium595yg/+gcRHhXHWxFTOmphKeUMrHx2qZcuRerYW17MwN5kLpqQTG6FvIaUClf7vDTKd3W7+tOkoByqbuWBKOhdOTfdJh+74xChWzM/koqnpvLu/ik2Ha9lxrIFLpo9hUW4yDtFOY6UCjQ4DDSKd3W7++PFRDlY2c/UZGVwyfYzPR/MkxYRzzbxM/unCfMYlRrJ6ZzmPvHeIqqY2n76OUmrkaQIIEsYYXthWSlFVM9fMy2BhTvKIvt6Y+EhuOTuXlQuzqG/p4KF3i9hQVIPbnGp8kVLKn2gTUJDYeLiW3WWNXDp9DPMnjOyHfw8RYXZmIrmpMby8vYzXd1dwuOYk183PJDJMRwsp5e/0CiAIHKtr4Y3dx5k6No6lk9NG/fXjIsO4ackErpg1jv3Hm3j43SIqtUlIKb+nCSDAdXS5WbXlGPFRLq6bn2VbZ6yIcPakVG45J4/2LjePrj/E4ZpmW2JRSnlHE0CAe/9AFQ0tnayYn+UXk7RyU2P4+nkTiYsI4/cbitlV2mB3SEqpAWgCCGC1ze2sP1jD3CxPO7y/SIoJ547z8shMjOLZLSVsP1Zvd0hKqX5oAghQxhhe21WB0yEsmznW7nA+IzrcxVfPziU3LYYXtpVqElDKD2kCCFBFVc3srzzBRVPTiY/0z2UZwl0Ovrwk59MksLus0e6QlFK9eJUARGSZiOwXkSIRubuf8xEi8qx1fpOI5PQ5ny0izSLyL74JW727v4qEqDDOnJhidyin1JMEslOieW5rCR8frrU7JKWUZdAEICJO4GHgMmA6cIOITO9T7Bag3hgzCXgQeKDP+QeBN4YfrgIorjlJcW0LS/NTcTn8/yIu3OXgpiUTSI4J57antrLveJPdISml8O4KYBFQZIw5bIzpAFYBy/uUWQ48ad1+AbhIxDMeUUQ+DxwGCnwTsnr/QDXR4U4WjNKEL1+IDnfx1bNyiA53cssftlLb3G53SEqFPG8SQAZQ0ut+qXWs3zLGmC6gEUgRkRjg+8CPhh+qAihvaGV/5QnOnpRKuMv/v/33lhgdzu++vJCa5na+8adP6Ox22x2SUiHNm0+Q/mYW9V3wZaAyPwIeNMacckaQiNwuIltFZGt1dbUXIYWuDw5WE+FysCTXv9v+BzIrM4GfXTuLTUfq+MnrhXaHo1RI82YtoFIgq9f9TKB8gDKlIuICEoA6YDGwQkR+DiQCbhFpM8Y81PvBxpjHgMcAFixYoKuJDaClo4uC8iYW5gTuzlw9+yGcMymVP3xUTGtnN3MyE71+/I2Ls0cqNKVCjjcJYAuQLyK5QBmwErixT5nVwM3ARmAFsM4YY4ClPQVE5D6gue+Hv/LejpIGutyGBTlJdocybJfOGMuxuhZe2V5GVlI0yTHhdoekVMgZtAnIatO/E1gLFALPGWMKROR+EbnKKvY4njb/IuA7wGeGiqrhMcaw7Wg9GYlRjEuIsjucYXM6hOsXZCECq7Yco8ut/QFKjTavloM2xqwB1vQ5dm+v223AdYM8x32nEZ+ylDe0UdHYxlVzxtsdis8kxYRzzRmZPLP5GO8UVnHpDP+b0axUMAusYSQhbOvROlwOGVJ7eSCYmZHA/AlJrD9QzbG6FrvDUSqkaAIIAJ3dbnaWNjAzIyFgO39P5YpZ44iPCuOFbaU6NFSpUaQJIAAcrGymrdPNGVnB9e2/R2SYk2vnZVLT3M5beyvtDkepkKEJIAAUlDcSFeYkLy3W7lBGzKT0WBblJrOhqIbSem0KUmo0aALwc11uN4XHm5g2Lg6nw57dvkbLshljiY108fL2MrrdOh1EqZGmCcDPHa4+SVunmxnjE+wOZcRFhjm5cvZ4Khrb+OhQjd3hKBX0NAH4uYLyJsJdDialB2/zT28zxsczdWwcbxdWUt/SYXc4SgU1TQB+zG0MeyuamDImjjBnaPxTiQhXWnMd3thz3OZolApuofGpEqCO1rZwsr2LmRnB3/zTW1J0OEvz09hT1siRmpN2h6NU0NIE4McKK5pwOYTJY0Kj+ae3c/PTSIgK4/Vd5biNdggrNRI0Afixg1UnyEmJIcIVfJO/BhPucrBsxljKG9v45KhuKK/USNAE4KcaWzupbGonPwS//feYnZnAhORo1u6tpK2z2+5wlAo6mgD8VFGVZw+dUBn90x8R4YrZ4zjZ3sW7+6vsDkepoKMJwE8drDpBXISLsfGRdodiq8ykaOZlJ/FRUa3uI6yUj2kC8ENuYyiqamZSeiwiwT371xufmzEGp1NYo8NClfIpTQB+qLyhlZaObvLHxNkdil+IjwzjgslpFFY06QxhpXxIE4AfOqjt/59x1qRUEqPC+Omafbh1nSClfEITgB86WNnM+MRIYiO82rAtJIQ5HVwyfQy7yxr5y65yu8NRKihoAvAznd1uSupamBjESz+frjlZicwYH8/P39xPe5cOC1VquDQB+JmSuha6jSE3NcbuUPyOQ4QfXj6NsoZWnvroqN3hKBXwNAH4mSO1JxFgQrImgP6cPSmV8yan8at1B2nQ1UKVGhZNAH6muOYkYxMig3LvX1/5weVTOdHexcPvFtkdilIBTROAH+lyuzlW10JOin77P5WpY+NZMS+TJz86Skmdbh+p1OnSBOBHyhva6Ow25Gj7/6C++7kpOBzwX3/db3coSgUsTQB+pNha+z4nJdrmSPzf2IRIbj0nj1d3lLOrtMHucJQKSJoA/Ehx7UlSYyOIiwyzO5SAcMd5eaTEhPOfawoxumeAUkOmCcBPuI2huPakfvsfgrjIMO66OJ+PD9fpaqFKnQZNAH6isqmNtk63jv8fohsWZZObGsNP1+yjq9ttdzhKBRRNAH7iaK1nNMsEHQE0JGFOB99fNoWDVc08v63U7nCUCiiaAPxEaX0LMeFOkqK1/X+oLp0xlvkTkviftw7Q0tFldzhKBQxNAH7iWF0rWcnRuv7/aRARfnj5VKpPtPPb9UfsDkepgKEJwA+0dnRT09xOVrJ2AJ+u+ROSuWzmWB5df4jqE7pzmFLe0ATgB0rqPe3/WUmaAIbje8um0tHl5n/fPmB3KEoFBE0AfqCkrgUBMpOi7A4loOWmxvDFxdms2lJCkbWpjlJqYF4lABFZJiL7RaRIRO7u53yEiDxrnd8kIjnW8UUissP62SkiV/s2/OBQUt9CWlwEkWG6ANxwfeuifKLCnDzw5j67Q1HK7w265ZSIOIGHgUuAUmCLiKw2xuztVewWoN4YM0lEVgIPANcDe4AFxpguERkH7BSRvxhjdKiGxRhDSV0rM8bH2x1KQHhm07FBy5w1MYW/7q3kJ68XfmZexY2Ls0cqNKUCjjdXAIuAImPMYWNMB7AKWN6nzHLgSev2C8BFIiLGmJZeH/aRgM7X76P2ZAetnd3aAexDZ01MJT7SxRt7KnDrEhFKDcibBJABlPS6X2od67eM9YHfCKQAiMhiESkAdgNf02//f69nOWPtAPadcJeDz00fS2l9K58crbc7HKX8ljcJoL+B6X2/Vg1YxhizyRgzA1gI/EBEIj/zAiK3i8hWEdlaXV3tRUjBo6S+hXCXg/T4CLtDCSpzsxPJSYnmjT3HaW7X7xxK9cebBFAKZPW6nwmUD1RGRFxAAlDXu4AxphA4Cczs+wLGmMeMMQuMMQvS0tK8jz4IlNa3kpEYhUMngPmUQ4TPz82go8vN67v6vl2VUuBFJzCwBcgXkVygDFgJ3NinzGrgZmAjsAJYZ4wx1mNKrE7gCcAUoNhXwQe6brfheGMbS/JS7A4lKKXHR3LelDTW7avijOwTTB4TN+zn9KYT+lS0E1r5k0GvAKw2+zuBtUAh8JwxpkBE7heRq6xijwMpIlIEfAfoGSp6Dp6RPzuAl4FvGGNqfF2JQFXZ1EaX25Ch4/9HzHmT00iLjeClT0p1nSCl+vDmCgBjzBpgTZ9j9/a63QZc18/jngaeHmaMQau8oRWAjERNACMlzOngCwuzeOS9Il7dUc4t5+TqektKWXQmsI1KG1qJcDlIjgm3O5SglpEYxcXTxrC7rJFXdpTZHY5SfkMTgI3KG7QDeLScOzmNCSnR/NsrBRRVnbA7HKX8giYAm3S53VQ0tmnzzyhxiLByYTaRYQ5ue2obja2ddoeklO00Adikqqmdbu0AHlUJUWH8+ovzKalr4Z+f3UG3W2cJq9CmCcAmZdoBbItFucn8+1UzWLevivtWF2B0qQgVwrwaBaR8r6y+lcgw7QC2w5cWZ1Na38Kj7x8mNtLF95dNtTskpWyhCcAmZQ2tjE+M0iGJNhAR7l42lea2Lh557xDhTgffvjhf/y1UyNEEYIMut5vjTW2cNVFnANtFRPjx8pm0d7n55TsHqT3Zzo+umonToUlAhQ5NADaoPuHpAB6v7f+2cjiEn187m9TYCH7z/iEqm9p58Pq5xEbofwsVGrQT2AblDW0AjE/QBGA3h0O4+7Kp3HfldN4prOTKX33I3vImu8NSalRoArBBRWMrYU4hJVY7gP3FV87O5c+3LeFkexdX/3oDT35UjFuHiaogpwnABhWNbYyNj9QZwH5mcV4Ka+5aypK8FP59dQFf/N2mTzfsUSoYaQIYZcYYKhpbtf3fT6XGRvCHry7kZ9fMYndZI8v+dz3PbDqm8wVUUNIEMMrqWzpp63QzTtv//ZaIsHJRNm9+eylzsxP54cu7+fITmz9dvVWpYKEJYJRVNHo+RMYlfGZnTOVnMpOi+eMti/nx52ey7Wg9lz64nq3FdXo1oIKGJoBRVt7QhgBjNQEEBBHhpiUTePOuc5k+Pp6Xtpfxp03HaOvstjs0pYZNE8Aoq2hsJS0ugjCn/ukDSXZKNH++bQmXzxzLvuNNPPLeIapPtNsdllLDop9Co6yisU2bfwKUwyGck5/GP56dy8mOLh55v4ijtSftDkup06YJYBTVn+ygsbVTO4ADXF5aLN+8YBKxES6e2HCEoqpmu0NS6rRoAhhFeys8M0x1CGjgS4oO57aleaTERPDkxmIOVOouYyrwaAIYRT1LDGgHcHCIiwzj1qW5pMdF8MzmYzpMVAUcTQCjaG9FE/GRLl1sLIhEh7u4+cwcosOcPLmxmIaWDrtDUsprmgBGUUF5ozb/BKH4qDBuPiuHzm43T208SkeX2+6QlPKKJoBR0tbZzaHqkzoCKEiNiY/khoXZVDa18dqucrvDUcormgBGyYHKE3S7jY4ACmL5Y+I4b0oaW4/Ws/1Yvd3hKDUoTQCjpKcDWJuAgttFU8eQkxLNqzvKqWnWiWLKv2kCGCUF5U3ERbhIjA6zOxQ1gpwO4fqF2Tgc8NInpbh13SDlxzQBjJK9FU1MGxevewCEgISoMK6YNY7i2hY2H6mzOxylBqQJYBS43YbCiiamj4+3OxQ1SuZlJ5GfHsube45Tf1KHhir/pAlgFBTXnqSlo1sTQAgRET5/RgYIvLqzTJeQVn5JE8Ao6FkCYvo4TQChJCk6nIunpnOgspl9x3WpCOV/NAGMgr3lTbgcQv6YWLtDUaPszImppMdF8Nqucjq7dYKY8i+aAEZBQXkT+WPiiHA57Q5FjTKnQ7hyznjqWzr54GC13eEo9Xc0AYyCvRVN2vwTwiamxTIrI4H39lfrgnHKr3iVAERkmYjsF5EiEbm7n/MRIvKsdX6TiORYxy8RkW0istv6faFvw/d/VSfaqD7RzgztAA5py2aOBeC//3rA5kiU+ptBE4CIOIGHgcuA6cANIjK9T7FbgHpjzCTgQeAB63gNcKUxZhZwM/C0rwIPFAXWDGBNAKEtKTqcMyem8NL20k9nhStlN2+uABYBRcaYw8aYDmAVsLxPmeXAk9btF4CLRESMMduNMT0rYxUAkSIS4YvAA0VBWSOADgFVnD85nYSoMH76RqHdoSgFeJcAMoCSXvdLrWP9ljHGdAGNQEqfMtcC240xn1kgRURuF5GtIrK1ujq4OsoKypuYkBJNXKQuARHqosKd3HnBJD44WMP7B4Lrfa4CkzcJoL+1C/rOajllGRGZgadZ6I7+XsAY85gxZoExZkFaWpoXIQWOgvImbf5Rn7rpzAlkJUfx0zWFdLt1cpiylzcJoBTI6nU/E+i74PmnZUTEBSQAddb9TOBl4MvGmEPDDTiQNLV1cqyuhRnjE+wORfmJCJeT7106lX3HT/DSJ6V2h6NCnDcJYAuQLyK5IhIOrARW9ymzGk8nL8AKYJ0xxohIIvA68ANjzAZfBR0oejr7tP1f9fYPs8cxJzOB//7rAVo7uu0OR4WwQROA1aZ/J7AWKASeM8YUiMj9InKVVexxIEVEioDvAD1DRe8EJgH/JiI7rJ90n9fCT+kIINUfEeGHl0/jeFMbT2w4Ync4KoR5tTu5MWYNsKbPsXt73W4Druvncf8B/McwYwxYBeWNpMVFkB6n20Cqv7c4L4WLp43hkfcOcf3CLFJjQ2pwnPITOhN4BO3VDmB1CndfNpXWzm5++fZBu0NRIUoTwAhp6+zmYFWzJgA1oEnpsdywKItnNh/jUHWz3eGoEKQJYIT0bAKvI4DUqXz74slEhTn52Rv77A5FhSBNACNEO4CVN1JjI/j6+RN5a28lmw7X2h2OCjFedQKroSsobyQuwkVWUrTdoSg/8symY585FhPuIiEqjO8+v5OvnTfxlPtG37g4eyTDUyFGrwBGSEF5E9PGx+Nw6Cbw6tTCXQ4umTaG0vpWdpc22h2OCiGaAEZAt9uwr+KENv8or83NTmRcQiRr9x7XncPUqNEEMAKO1DTT2tmtHcDKaw4RLps5joaWTjYe0r4ANTo0AYwA7QBWp2NSeixTx8axbn8VTa2ddoejQoAmgBFQUN5EuMvBpHTdBF4NzRWzxuF2G9bsqbA7FBUCNAGMgILyRqaMiSPMqX9eNTQpsRGcOzmNXaWNOjlMjTj9hPIxY4zuAaCG5bzJaSRFh7F6Zzld2iGsRpAmAB8rb2yjoaVTE4A6bWFOB1fNGU/1iXbe053D1AjSBOBjez7dA1hHAKnTN2VsPGdkJfLe/irKG1rtDkcFKZ0J7GMF5U2IwLRxcXaHovrR30xcf3XF7HEcrGrmxU9K+cb5k3DqpELlY3oF4GMFZY3kpcYQHa65VQ1PdLiL5XPHU9HYxrp9VXaHo4KQJgAfMsaws7SROVmJdoeigsSM8QnMy/Y0BemoIOVrmgB8qKKxjZrmduZkagJQvnPlnPGkxEbw3NYSaprb7Q5HBRFNAD60s6QBQK8AlE9FuJzcsCiL1o5u/vnZHTo0VPmMJgAf2lnaSJhTtANY+dy4hCiunDOeDw7W8JM1hXaHo4KE9lT60M6SBqaNiyfC5bQ7FBWEFuYkkxQdzhMbjjAxLZYvLZlgd0gqwOkVgI+43YbdZY3MztTx/2rk3HPFNC6Yksa/ry7gncJKu8NRAU4TgI8crmmmub1LO4DViHI6hP+74QxmjI/n63/8hA8O6kxhdfo0AfjIzhLPDGDtAFYjLS4yjKf+cRF5aTHc9tRWPta9hNVp0gTgIztLG4gJdzIxTZeAViMvMTqcP966mIzEKG5+YjPr9mlzkBo6TQA+srO0kZkZCTpdX42a1NgInrvjTCaPieO2p7bx0ieldoekAowmAB/o6HJTWN7EXG3+UaMsJTaCP9++hMW5yXznuZ38Yu0+3G5jd1gqQGgC8IE95Y10dLs5I1sTgBp9sREufv/VhaxcmMXD7x7i1qe20tSmW0qqwWkC8IFtxfUAzJuQZHMkKlRFuJz89JpZ/Hj5DNYfqObzD2/QtYPUoHQimA9sPVpHdnI06XGRdoeigtxgy1k7HQ6+enYuz2w6yuW//IDrF2QxdZxnc6IbF2ePRogqgOgVwDAZY9h2tJ4F+u1f+Ync1Bi+ecEkUmLCeerjo7xdWInbaL+A+ixNAMN0tLaFmuYO5udoAlD+IzE6nDvOm8i87ETW7aviqY3F1J/ssDss5Wc0AQzT1qOe9v8FE5JtjkSpvxfmdHDtvEyWzx3PoeqT/MOvPmR3aaPdYSk/4lUCEJFlIrJfRIpE5O5+zkeIyLPW+U0ikmMdTxGRd0WkWUQe8m3o/mHb0TriI13kp+sEMOV/RITFuSncvjQPYwzX/uYjXtym8wWUx6AJQEScwMPAZcB04AYRmd6n2C1AvTFmEvAg8IB1vA34N+BffBaxn9laXM+8CUk4dAKY8mNZydG89q2lLJiQxHef38l/rd2v8wWUV1cAi4AiY8xhY0wHsApY3qfMcuBJ6/YLwEUiIsaYk8aYD/EkgqDT2NLJwapm5mdr+7/yf8kx4Tz5j4tYuTCLh94t4lurttPW2W13WMpG3gwDzQBKet0vBRYPVMYY0yUijUAKUOOLIP3VJ8c87f/aAawCQc8Q0lkZCTS0dPL6rgp2lTbypSUTiI0Y/KNAh5EGH2+uAPpr2+h77ehNmYFfQOR2EdkqIlurqwNnedtNR+pwOUSXgFABRUQ4d3IaNyzKpqKxlUfeK6L6hO41HIq8SQClQFav+5lA+UBlRMQFJAB13gZhjHnMGLPAGLMgLS3N24fZ7qNDNZyRnUh0uM6nU4FnZkYCty3No6Pb8Nj6Q1Q0ttodkhpl3iSALUC+iOSKSDiwEljdp8xq4Gbr9gpgnTHBPfOksaWT3WWNnDUx1e5QlDptmUnR3LE0D5fTwW8/OMyxuha7Q1KjaNAEYIzpAu4E1gKFwHPGmAIRuV9ErrKKPQ6kiEgR8B3g06GiIlIM/A/wFREp7WcEUUDaeLgWY+DsSZoAVGBLjYvg9nPziA538cSHR3QNoRDiVduFMWYNsKbPsXt73W4DrhvgsTnDiM9vfXSohqgwp7b/q6CQFB3O7efm8cSHR3jyo2K+uDibKWPj7Q5LjTCdCXyaNhTVsCg3mXCX/glVcIiPDOO2pXmkx0fw9MdH2VOms4aDnX56nYbjjW0cqj7J2ZNS7A5FKZ+KiXBx6zl5ZCZFs2rLMXZrEghqmgBOw0eHPNMbtANYBaPIMCdfOSuHrKRont1yjF2lDXaHpEaIJoDTsKGolqToMKaP0zZSFZx6kkB2cjTPbilhR4kmgWCkCWCI3G7Dh0XVnDkxRdf/UUEtIszJV87KJSc1hue3luim80FIE8AQ7SlvpLKpnYumjrE7FKVGXLjLwc1n5pCXFsN3n9/J81tLBn+QChiaAIborb2VOAQumJpudyhKjYpwl4Mvn5nDOZNS+d6Lu1i1+dTbUqrAoQlgiN7aW8mCnGSSY8LtDkWpURPmdPDbLy/g3Pw07n5pN3/adNTukJQPaAIYgpK6FvYdP8El07T5R4WeyDAnj940nwumpHHPy3t4amOx3SGpYdIEMARvF1YCcPF0TQAqNEWGOfnNTfO5eFo6975awOMfHrE7JDUMmgCG4K29lUxKjyU3NcbuUJSyTYTLya+/OJ9LZ4zhx6/t5Rdr9xHkaz8GLU0AXmps6WTTkTou0W//ShHucvDwjfNYuTCLh989xPde2EVnt9vusNQQ6UL2Xlq79zjdbsPnNAEoBYDL6eCn18wiPS6C/1tXRHljK7++cT4J0WF2h6a8pFcAXnpxWym5qTG6+qdSvYgI3/ncFH6xYjabj9Rx9a83cFiXkw4YmgC8UFLXwqYjdVxzRgYiOvtXqb6uW5DFn25dQn1LB1c9tIE3dlfYHZLygkCD8ikAAA9DSURBVCYAL7y8vQyAq+dl2ByJUv5rUW4yr31rKRPTY/n6nz7hR38poK2z2+6w1CloAhiEMYaXPinlzLwUMpOi7Q5HKb+WkRjF83ecyVfOyuH3G4q56qEPdV8BP6adwIPYdrSe4toW7rww3+5QlLLVM5u8XwJi8pg4bj4zh5e2l3LVQx9ybn4aj3xpPlHhzhGMUA2VXgEM4rmtJUSFOVk2c6zdoSgVUKaMjeOui/KZk5nIewequeTB91lbcFznDPgRTQCnUHWijVe2l3P1vAxiI/RiSamhig53cd2CLG5dmktUmJM7nt7Git9sZPOROrtDU2gCOKU/bCim0+3m9qV5doeiVEDLS43ljbuW8p9Xz6KkroUvPLqR6x/dyPoD1XpFYCP9WjuA5vYunv74KJfNHEuOLv2g1LC5nA5uXJzN1Wdk8KdNR/ndB0f48hObyU+P5YZF2VwzL4PEaF1ldzTpFcAAVm0+xom2Lu44d6LdoSgVVKLCndy6NI/3v3c+P792NtERLu5/bS+L//MdvvPsDrYU1+lVwSjRK4B+tHV28/iHR1iSl8wcnfmr1IiIcDn5wsIsvrAwi4LyRlZtLuGV7WW8tL2M7ORols8dz/K5GUxKj7U71KClCaAfv11/mIrGNv77C3PsDkWpkDBjfAI//nwCP7h8Kmt2H+fVHWU8/G4Rv1pXxIzx8Xx+bgZdbkNC1OmvM3Tj4mwfRhwcNAH0UVrfwsPvFXHFrHGcNTHV7nCUCinR4S5WzM9kxfxMqk608drOCl7dWc5P1hQiQG5qDHOyEpk5PkHnFPiAJoA+fvJ6IQA/vGKazZEoFVyGMpGsR2SYk+sXZHHR1HR2ljaws6SBl7eXsXpnOVPGxDEnK5GpY+MIc2p35unQv1ov7+2v4o09x/nm+ZPISIwakdf44cu7vT4+lLK+eA6Nzb/K+kts//ZK/8fv6af8QGV7dtPr7Ud/2dNv2QfeLPzMsdTYCC6aOoZ/vngy3zh/Iktykympb+HPm4/xn2sKeWFbKQerTuC2Oo/7e70H3zrQ7+uFMk0AlrKGVr7z3E7y02O57Vwd969Uj+4BBuT0d3igsuv2VX3mWHtX/4UbW7sGjEVEyEyK5orZ4/n+sqkAzByfQEF5I7/fUMzP39zHW3sr+329X75zcMDnDVXaBIRn1M/X/7iNji43v7lpPpFh2raolL9zWEuzXzs/k6vmjmff8RNsO1rHe/s9H/6/33CEhTnJTBsXj9Ohy7j3J+QTgNttuOflPewqbeTRm+YzMU2HnCkVaMKcDmZlJDArI4GGlg5+vnY/VSfaeWbzMWIiXMzP1uHc/QnpJqDObjf//NwOXvyklG9fnM+lM3TBN6UCXc9s4n+9dAo3nzmBCcnRfFhUA8DKxzby6o4y3afAErJXACfaOvn2qh28s6+K7y2bwjfOn2R3SEopH3KIMGVsPFPGxtPU1snP3thHeUMbd63aQWJ0GFefkcGK+ZlMHxcfsjv9hWQCWH+gmrtf3EVFUxs/Xj6Dm87MsTskpdQIio/0TCB771/OZ+PhWp7ZfIw/fnyU328oZkJKNMtmjuXymeOYnZkQUsnAqwQgIsuAXwJO4HfGmJ/1OR8BPAXMB2qB640xxda5HwC3AN3At4wxa30W/RDtKm3g0fcP8/ruCiamxfDC185i/oQku8JRSo0yh0M4e1IqZ09Kpe5kB38tOM6aPcd5/IMjPPr+YTISozh3cipL8lJYnJvC2IRIu0MeUYMmABFxAg8DlwClwBYRWW2M2dur2C1AvTFmkoisBB4ArheR6cBKYAYwHnhbRCYbY0atAe5o7UneLqzijd0VbD1aT1yEi29dOIlvXDBJR/soFcKSY8JZuSiblYuyaWjp4O3CKt7cc5zXdlbw580lAExIiWaRNZJo8pg48sfEkh4XETRXCd5cASwCiowxhwFEZBWwHOidAJYD91m3XwAeEs9faDmwyhjTDhwRkSLr+Tb6Jvy/aWzt5JOj9ZQ2tFJW38qByhPsLmuk+kQ7APnpsdxz+TRWLsoiLvL01xNRSgWfxOjwT5eg6HYbCiua+PhwLZuO1PF2YSXPbyv9tGx8pIu8tFjGxkeSHh/BmPhI0uIiSIkJJybCRaz1ExPhIiLMgVMEp0NwfPrbM5/BGEOX29DR5aajy017l5swp5ASGzFq9fYmAWQAJb3ulwKLBypjjOkSkUYgxTr+cZ/HZpx2tKdwpOYkX/3DFgDCnEJuagxL81OZnZHABVPTmZCia/orpQbndAgzMxKYmZHArUvzMMZQ09zBwcoTHKxq5mDVCY7UnKSoupmPDtXQ1DbwxLWB9ExLcPeZC3flnPH86oYzfFAL78hg626LyHXApcaYW637NwGLjDH/1KtMgVWm1Lp/CM83/fuBjcaYP1rHHwfWGGNe7PMatwO3W3en4OlHqBl+9fxSKsFbN9D6BbJgrhsEf/2mGGPihvIAb64ASoGsXvczgfIBypSKiAtIAOq8fCzGmMeAx3rui8hWY8wCbyoQaIK5bqD1C2TBXDcIjfoN9THeTATbAuSLSK6IhOPp1F3dp8xq4Gbr9gpgnfFcWqwGVopIhIjkAvnA5qEGqZRSyvcGvQKw2vTvBNbiGQb6hDGmQETuB7YaY1YDjwNPW528dXiSBFa55/B0GHcB3xzNEUBKKaUG5tU8AGPMGmBNn2P39rrdBlw3wGN/AvxkiHE9NniRgBXMdQOtXyAL5rqB1u8zBu0EVkopFZxCejE4pZQKZX6TAETkOhEpEBG3iCzoc+4HIlIkIvtF5FK7YhwuEVlm1aFIRO62O57hEpEnRKRKRPb0OpYsIm+JyEHrd0CutSEiWSLyrogUWu/Lu6zjwVK/SBHZLCI7rfr9yDqeKyKbrPo9aw38CEgi4hSR7SLymnU/mOpWLCK7RWRHz+if03lv+k0CAPYA1wDrex/ss5zEMuDX1vIUAaXXkhqXAdOBG6y6BbI/4Pk36e1u4B1jTD7wjnU/EHUB3zXGTAOWAN+0/r2CpX7twIXGmDnAXGCZiCzBs4zLg1b96vEs8xKo7gJ67y8ZTHUDuMAYM7fX0NYhvzf9JgEYYwqNMfv7OfXpchLGmCNAz3ISgebTJTWMMR1Az5IaAcsYsx7PqK/elgNPWrefBD4/qkH5iDGmwhjziXX7BJ4PkgyCp37GGNNs3Q2zfgxwIZ7lXCCA6ycimcAVwO+s+0KQ1O0Uhvze9JsEcAr9LUUxIstJjLBgqcdgxhhjKsDzIQqk2xzPsIlIDnAGsIkgqp/VRLIDqALeAg4BDcaYnrUNAvk9+r/A9wC3dT+F4KkbeJL1X0Vkm7WSApzGe3NU9wMQkbeB/rbduscY8+pAD+vnWCAOXQqWeoQUEYkFXgS+bYxpCpZVIAGsOTlzRSQReBmY1l+x0Y1q+ETkH4AqY8w2ETm/53A/RQOubr2cbYwpF5F04C0R2Xc6TzKqCcAYc/FpPMyr5SQCQLDUYzCVIjLOGFMhIuPwfLsMSCIShufD/0/GmJesw0FTvx7GmAYReQ9PX0eiiLisb8qB+h49G7hKRC4HIoF4PFcEwVA3AIwx5dbvKhF5GU8T85Dfm4HQBBQsy0l4s6RGMOi9LMjNwEBXdn7NajN+HCg0xvxPr1PBUr8065s/IhIFXIynn+NdPMu5QIDWzxjzA2NMpjEmB8//s3XGmC8SBHUDEJEYEYnruQ18Ds8gmqG/N40xfvEDXI3nW3I7UAms7XXuHjztk/uBy+yOdRh1vBw4YNXlHrvj8UF9/gxUAJ3Wv90teNpa3wEOWr+T7Y7zNOt2Dp4mgl3ADuvn8iCq32xgu1W/PcC91vE8PF+wioDngQi7Yx1mPc8HXgumuln12Gn9FPR8lpzOe1NnAiulVIgKhCYgpZRSI0ATgFJKhShNAEopFaI0ASilVIjSBKCUUiFKE4BSSoUoTQDKr1jL3KbaHYevich7fZc5P83nSRSRb5zi/E9EpEREmgcqo1QPTQBKBZZEYMAEAPyFwFwtV9lAE4CyjTWl/XVrU5I9InJ9r3NRIvKmiNxm3f+StYHJDhF51FrJ8gsi8j/W+btE5LB1e6KIfGjdLhaRH4nIJ9YGGlN7vfYTIrLF2jRkuXV8Rq/X2SUi+aeKs5863Ws95x4ReUz+fvW4L4nIR9a5RVb5ZBF5xXqtj0VktnX8PhH5l17Pu8dalfRnwEQrvl/0fX1jzMfGWhFSqcFoAlB2WgaUG2PmGGNmAm9ax2PxfJN9xhjzWxGZBlyPZwXEuUA38EU8mwcttR6zFKgVkQw8yzh80Ot1aowx84BHgJ4P1XvwrBGzELgA+IW1rsrXgF9ar7MAzxIXA8XZn4eMMQutclHAP/Q6F2OMOQvPN/gnrGM/ArYbY2YDPwSeGuRvdjdwyHg2AvnXQcoqdUqaAJSddgMXi8gDIrLUGNNoHX8V+L0xpufD8CJgPrDFWr/+IiDPGHMciLUWxsoCngHOxZMMeieAnpU8twE51u3PAXdbz/cenlUjs4GNwA9F5PvABGNM6yni7M8F4tl2cDeeDUhm9Dr3Z/h0I514azG2c4CnrePrgBQRSRj0L6eUD2gCULYxxhzA88G+G/ipiNxrndoAXNar+USAJ61vvXONMVOMMfdZ5zYCX8WzUOAHeD78z7Seo0e79bubvy2BLsC1vZ4z23h2pXsGuApoBdaKyIWniPPviEgk8GtghTFmFvBbPInl0yr3/RMw8Dr1Xfz9/8/IfsopNSyaAJRtRGQ80GKM+SPwX8A869S9QC2eD1PwrGy4wtr8oqfdfIJ1bj2eZp31eFa3vABoH+RbOsBa4J96koyInGH9zgMOG2P+D8/yurNPEWdfPR/SNeLZSGZFn/PXW69xDtBoxbgeT3MW4tm8pMYY0wQU97yOiMwDcq3nOAHEDVI3pbyiCUDZaRaw2WqGuQf4j17nvg1EisjPjTF7gf+HZwu8XXi2LxxnlfsAT/PPeuPZ4aoE+NCL1/4xnn1wd4nIHus+eD6k91gxTcXTJn+qOD9ljGnA861/N/AKnj0geqsXkY+A3/C3DcnvAxZY9foZf1vP/UUg2XrNr+NZRhxjTC2wweoU/gWAVQbr9s9FpBSIFpFSEbnPi7+FClG6HLRSSoUovQJQSqkQNap7AisVLMSzD2tun8PfN8astSMepU6HNgEppVSI0iYgpZQKUZoAlFIqRGkCUEqpEKUJQCmlQpQmAKWUClH/Hwcmk832lpYhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['skewness_about.1'], rug=True)\n",
    "#\"skewness_about.1\" variable has slightly right skewed  distribution across the data set \n",
    "#Data concentration can be seen between value ranges-0-40 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3966,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa239f090>"
      ]
     },
     "execution_count": 3966,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-skewness_about.1 vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['skewness_about.1'])\n",
    "#Inference: From below graph, it is evident that skewness_about.1 is almost overlapping for each class\n",
    "#car has wide spread range w.r.t skewness_about.1\n",
    "#van and bus has outliers w.r.t skewness_about.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3967,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyzing variable-skewness_about.2\n",
    "#This variable showed presence of nan values previously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3968,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([187., 189., 188., 199., 180., 181., 200., 193., 195., 194., 196.,\n",
       "       197., 186., 198., 185., 179., 192., 191., 190., 183., 184., 202.,\n",
       "       201., 182., 176., 178., 203., 177.,  nan, 204., 206.])"
      ]
     },
     "execution_count": 3968,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about.2\"].unique()\n",
    "#Inference, we could see presence of nan values.  Before replacing the 'nan' values\n",
    "#Let's check if 'skewness_about.2' variable has any outliers \n",
    "#as this will impact the replacement value of nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3969,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa262dcd0>"
      ]
     },
     "execution_count": 3969,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles['skewness_about.2'])\n",
    "#Boxplot does  not show the presence of outliers for skewness_about.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3970,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets replace na values with new median\n",
    "vehicles[\"skewness_about.2\"].fillna((vehicles[\"skewness_about.2\"].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3971,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([187., 189., 188., 199., 180., 181., 200., 193., 195., 194., 196.,\n",
       "       197., 186., 198., 185., 179., 192., 191., 190., 183., 184., 202.,\n",
       "       201., 182., 176., 178., 203., 177., 204., 206.])"
      ]
     },
     "execution_count": 3971,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles[\"skewness_about.2\"].unique()\n",
    "#Inference: nan is replaced with median and hence 'nan' it is not displayed as one of the unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3972,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa26f9350>"
      ]
     },
     "execution_count": 3972,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles['skewness_about.2'], rug=True)\n",
    "#\"skewness_about.2\" variable has slightly right skewed  distribution across the data set with multiple modes\n",
    "#Data concentration can be seen between value ranges-176-204 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3973,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa288ed90>"
      ]
     },
     "execution_count": 3973,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAcB0lEQVR4nO3de5hV5Xn38e9vBAVFY2DwUBBHAxhjPESmNkeDRIzERJNUjaQ18zZWGrUSYkzjmwAqHqqJpHG01WI0ojUkNWokFl4lXhoPr4eCHD1URiXJKFUO4VQRB7n7x14T9x5nYK9hr1mzZ36f65pr1vOsw9zjvuSeZ61n3Y8iAjMzs1Y1eQdgZmbdixODmZmVcGIwM7MSTgxmZlbCicHMzEr0yTuAnVVbWxt1dXV5h2FmVlUWLFiwOiIGt7ev6hNDXV0d8+fPzzsMM7OqIul3He3zrSQzMyvhxGBmZiWcGMzMrIQTg5mZlXBiMMvI6tWrOf/881mzZk3eoZil4sRglpGZM2eyZMkSZs6cmXcoZqk4MZhlYPXq1cydO5eIYO7cuR41WFVxYjDLwMyZM2ktab9t2zaPGqyqODGYZWDevHm0tLQA0NLSwgMPPJBzRGblc2Iwy8DYsWPp27cvAH379uWEE07IOSKz8jkxmGWgoaEBSQDU1NTQ0NCQc0Rm5XNiMMtAbW0t48aNQxLjxo1j0KBBeYdkVrZME4OkAyQ9JOl5Sc9K+mbSP1DSPEnLk+/vT/pHS1ovaVHyNTXL+Myy1NDQwBFHHOHRglUdtc6cyOTi0v7A/hHxjKQ9gQXAF4H/A6yNiKskXQS8PyK+K2k0cGFEfL7cn1FfXx+urmpmlo6kBRFR396+TEcMEbEyIp5JtjcCzwNDgFOA1vl7MykkCzMz6wa67BmDpDrgI8BTwL4RsRIKyQPYp+jQj0laLGmupMM6uNYESfMlzV+1alXGkZuZ9S5dkhgkDQDuAiZFxIbtHPoMcGBEHAlcB/yqvYMiYkZE1EdE/eDB7S5AZGZmnZR5YpDUl0JSuCMi7k66X0+eP7Q+h3gDICI2RMSmZHsO0FdSbdYxmmXBRfSsWmU9K0nAzcDzEfGjol2zgdapGg3Avcnx+yXnIOmYJD7/X2VVyUX0rFplPWL4BHAmMKZoCurngKuAsZKWA2OTNsCpwDJJi4FG4IzIctqUWUZcRM+qWZ8sLx4RjwHqYPdn2jn+euD6LGPKQmNjI01NTRW/bnNzMwBDhw6t+LWHDx/OxIkTK35dK2iviN4FF1yQc1Rm5fGbz93Y5s2b2bx5c95hWCe4iJ5Vs0xHDL1FVn95t163sbExk+tbdsaOHcucOXNoaWlxET2rOh4xmGXARfSsmjkxmGWgtraWD37wgwAceuihLqJnVcWJwSwjS5YsAWDRokU5R2KWjhODWQbuueeekvbs2bNzisQsPScGswz8+Mc/LmlPnz49p0jM0nNiMMtA2/cy/Z6mVRMnBrMMtM5I6qht1p05MZhlYNKkSSXtb3/72zlFYpaeE4NZBr70pS+VtE8++eScIjFLz4nBLCO77rorALvttlvOkZil48RgloGnn36at99+G4AtW7awYMGCnCMyK59rJZll4JJLLilpT5kyhTlz5uQTTA9WjZWNoftXN3ZiMMvApk2bttu27q23VzV2YjDLwIABA0qSwYABA3KMpudyZeNs+BmDWQba3kq67LLL8gnErBOcGMwycMwxx5S0R40alVMkZuk5MZhlwEX0rJo5MZhlwEX0rJo5MZhlwEX0rJo5MZhlwEX0rJo5MZhlwEX0rJo5MZhlwEX0rJplmhgkHSDpIUnPS3pW0jeT/oGS5klannx/f9IvSY2SmiQtkXR0lvGZZWngwIEADBo0KOdIzNLJesSwFfh2RBwKfBQ4T9KHgIuAByNiBPBg0gYYB4xIviYAN2Qcn1kmXnzxRdauXQvAmjVrMqnnY5aVTBNDRKyMiGeS7Y3A88AQ4BRgZnLYTOCLyfYpwG1R8CSwt6T9s4zRLAuXX355SXvatGk5RWKWXpc9Y5BUB3wEeArYNyJWQiF5APskhw0B/lB0WnPS1/ZaEyTNlzR/1apVWYZt1ikrVqzYbtusO+uSxCBpAHAXMCkiNmzv0Hb63jMBPCJmRER9RNQPHjy4UmGaVUxdXd1222bdWeaJQVJfCknhjoi4O+l+vfUWUfL9jaS/GTig6PShwGtZx2hWaZMnTy5pT506NadIzNLLelaSgJuB5yPiR0W7ZgMNyXYDcG9R/9eS2UkfBda33nIyqyYjR4780yihrq6O4cOH5xuQWQpZjxg+AZwJjJG0KPn6HHAVMFbScmBs0gaYA7wMNAE3AedmHJ9ZZiZPnswee+zh0YJVnUwX6omIx2j/uQHAZ9o5PoDzsozJrFhWS0NCYXnI/v37Z7LYS3dfGtKqm1dwM8tIb18e0qqXE4P1aln+1d3bl4e06uVaSWZmVsKJwczMSjgxmJlZCScGMzMr4cRgZmYlnBjMzKyEE4OZmZVwYjAzsxJODGZmVqKsxCDps5LOShbbKe7/ehZBmZlZfnaYGCRdCXwfOBx4UNL5Rbv/PqvAzMwsH+WMGL4AjImIScAoYJykf0r2dVQ51czMqlQ5iaFPRGwFiIh1FBLFXpLuBHbNMjgzM+t65SSGlyR9urUREe9ExFnAfwGHZhaZmZnlopzEcBrwdNvOiJhM6frMZmbWA+xwPYaI6HC1kYh4tbLhmJlZ3nbqPQZJCysViJmZdQ87lRgi4iOVCsTMzLqHshODpKvL6TMzs+qWZsQwtp2+cZUKxMzMuocdPnyWdA5wLnCwpCVFu/YEHs8qMDMzy8cOEwPwM2Au8I/ARUX9GyNibSZRmZlZbnZ4Kyki1kfECuC7QBR9DZA0bHvnSrpF0huSlhX1HSnpCUlLJf1a0l5Jf52kzZIWJV837swvZmZmnVPOiKHVf1BICAL6AQdRePv5sO2ccytwPXBbUd9PgAsj4rdJddbvAFOSfS9FxFEpYjIzsworOzFExOHFbUlHA3+3g3MeaVuqGzgEeCTZngfcz7uJwcx6oMbGRpqamvIOo2zLly8HYOLEiTlHks7w4cMrEnOaEUOJiHhG0p934tRlwMnAvRTKbRSX1TgoeWluAzA5Ih5t7wKSJgATAIYN2+7dLDPrBpqamlj47ELYO+9IyrSt8G3hq1X0Du+6yl2q7MQg6YKiZg1wNLCqEz/z60CjpKnAbODtpH8lMCwi1kgaBfxK0mERsaHtBSJiBjADoL6+PjoRg5l1tb1h2+hteUfRY9U8XLkFOdOMGPYs2t5K4ZnDXWl/YES8AJwAIGkkcFLSvwXYkmwvkPQSMBKYn/ZnmJlZ56V5xnApgKQ9C83Y1JkfKGmfiHhDUg0wGbgx6R8MrI2IdyQdDIwAXu7MzzAzs85LUxLjw8n9/2XAs5IWSPrwDs6ZBTwBHCKpWdJZwHhJLwIvAK8BP00OPxZYImkx8EvgG35Pwsys66W5lTQDuCAiHgKQNDrp+3hHJ0TE+A52XdvOsXfRiVtTZmZWWWmeVuzRmhQAIuJhYI+KR2RmZrlKM2J4WdIU4Pak/dfAK5UPyczM8pRmxPB1YDBwN3BPsv03WQRlZmb5STMr6Y/AREnvA7ZFxMbswjIzs7ykmZX055KWAouBpZIWJy+imZlZD5LmGcPNwLmtZSokfZLCVNMjsgjMzMzykSYxbCyuXRQRj0mqmttJ1VbEC1zIy8zyUc4Kbkcnm09L+ldgFoXy218BHs4utMpqampi4dLn2Lb7wLxDKZveLpSBWvDSf+ccSflq3vQ7iWbVrpwRw/Q27YuLtquqgN223Qfy1oc+n3cYPVq/5+7LOwQz20k7TAwRcVxXBGJmZt1DqvUYJJ1EYcW2fq19ETGt0kGZmVl+0kxXvZHCc4XzKSzveRpwYEZxmZlZTtKMGD4eEUdIWhIRl0qaTuEtaLPMeVZZ1/CMMoN0iWFz8v1NSX8GrAEOqnxIZu/V1NTEi8ueYdiAd/IOpWy7thQG5G+t+M+cIynP7zftkncI1k2kSQz3Sdob+CHwDIUZSTdlEpVZO4YNeIfJ9Z1aH8rKcPn8AXmHYN1EmlpJlyWbd0m6D+gXEetb90saGxHzKh2gmZl1rU6tHh0RW4qTQuLqCsRjZmY561Ri6IAqeC0zM8tJJRNDVb0FbWZm7atkYjAzsx6gkolhRQWvZWZmOUnz5vNpkvZMtidLuruo8ioR8eUsAjQzs66VZsQwJSI2Jgv0fBaYCdyQTVhmZpaXNImh9ZXTk4AbIuJeYNfKh2RmZnlKkxheTRbqOR2YI2m3HZ0v6RZJb0haVtR3pKQnJC2V9GtJexXt+7+SmiT9l6TPpv1lzMxs56VJDKcD9wMnRsQ6YCDwnR2ccytwYpu+nwAXRcThwD2t15D0IeAMCmW9TwT+RZKLt5iZdbE0iWF/4D8iYrmk0RTKbj+9vRMi4hGg7VqPhwCPJNvzgL9Mtk8Bfp68Vf0K0AQckyI+MzOrgDSJ4S7gHUnDgZspVFb9WSd+5jLg5GT7NOCAZHsI8Iei45qTvveQNEHSfEnzV61a1YkQzMysI2kSw7aI2Ap8GfhxRHyLwigira8D50laAOwJvJ30t1dSo923qSNiRkTUR0T94MGDOxGCmZl1JE3Z7RZJ44GvAV9I+vqm/YER8QJwAoCkkRRmOUFhhHBA0aFDgdfSXt/MzHZOmhHD3wAfA66IiFckHQT8W9ofKGmf5HsNMBm4Mdk1GzhD0m7JtUewg2cYZmZWeWnWY3hO0neBYUn7FeCq7Z0jaRYwGqiV1AxcDAyQdF5yyN3AT5PrPSvp34HngK3AeRFRPct1mZn1EGUnBklfAK6h8FLbQZKOAqZFxMkdnRMR4zvYdW0Hx18BXFFuTNZ7NDc38z8bd/EqYxn63cZd2KO5Oe8wrBtIcyvpEgrTR9cBRMQivOazmVmPk+bh89aIWC+VTB7yGgzWJYYOHcpbW1d6zecMXT5/AP2GDs07DOsG0iSGZZK+CuwiaQQwEfj/2YRlZmZ5SXMr6XwK5Sq2ALOADcCkLIIyM7P8pJmV9Cbw/eTLzMx6qDSzkkYCFwJ1xedFxJjKh2VmZnlJ84zhTgovo/2Ed9dmqBrNzc3UvLmefs/dl3coPVrNm2tobt6adxhmthPSzkryim1mZj1cmsTwa0nnUlhDYUtrZ0S0LavdLQ0dOpTXt/ThrQ99Pu9QerR+z93H0KH75R2Gme2ENImhIflevDhPAAdXLhwzM8tbmllJfsvZzKwXSDMr6VEKK689CjweERszi8rMzHKT9lbSJyksxflDSVuAR5MFe8wy9/tN1VVE7/U3C++P7rv7tpwjKc/vN+3CyLyDsG4hza2klyVtprDi2tvAccChWQVmVmz48OF5h5Da28uXA9CvbkTOkZRnJNX539kqL82tpJeA1RTWeb4ZOD8iquNPIat6EydOzDuE1FpjbmxszDkSs3TS1EpqBH4PjKdQQK9B0gcyicrMzHJTdmKIiGsj4jTgeGABhfUZXswoLjMzy0maW0nTKTx8HgA8AUylMEPJzMx6kDSzkp4EfhARr2cVjJmZ5S9NYrgL+KqkgyLiMknDgP0i4umMYjOzHqK5uRnWQ83DaR5rWirroDkqs2Z3mk/pn4GPAV9N2huTPjMz60HSjBj+IiKOlrQQICL+KGnXjOIysx5k6NChrNIqto32DPes1Dxcw9AhlVmzO82IoUXSLhQK5yFpMOBP2cysh0n7HsM9wD6SrgAeA/5xeydIukXSG5KWFfUdJelJSYskzZd0TNI/WtL6pH+RpKmd+H3MzGwnpSmJcYekBcBnAAFfjIjnd3DarcD1wG1FfT8ALo2IuZI+l7RHJ/sejQgvmGBmlqM07zGcFRE3Ay8U9V0VERd1dE5EPCKprm03sFey/T7gtbKjNTOzzKV5+HyqpLci4g4ASf8C7NaJnzkJuF/SNRRuZX28aN/HJC2mkCwujIhnO3F9MzPbCWkSw5eB2ZK2AeOAtRFxbid+5jnAtyLiLkmnUyjIdzzwDHBgRGxKbjH9Cmi3LKWkCcAEgGHDhnUiBDMz68gOHz5LGihpINAf+FvgH4ANwLSkP60G4O5k+07gGICI2BARm5LtOUBfSbXtXSAiZkREfUTUDx48uBMhmJlZR8oZMSwgmaKaEHAS8LmknXbN59eATwMPA2OA5QCS9gNej4hIZirVAGtSXtvMzHbSDhND61rPyW2f/xcRGyRNAY4GLtveuZJmUZhxVCupGbgYOBu4VlIf4C2SW0LAqcA5krYCm4EzIiLee1UzM8tSmmcMkyPi3yV9EhgLTAduAP6ioxMiYnwHu0a1c+z1FKa2mplZjtIkhneS7ycBN0bEvZIuqXxI2al5cy39nrsv7zDKprc2ABD99trBkd1HzZtrgf3yDsPMdkKaxPCqpH+lMIPoakm7ke7N6VxV41q2y5dvBGDEB6rpH9r9qvK/tZm9K01iOB04EbgmItZJ2h/4TjZhVZ7XDDYzK0+akhhv8u40UyJiJbAyi6DMzCw/VXMryMzMuoYTg5mZlXBiMDOzEk4MZmZWwonBzMxKODGYmVkJJwYzMyvhxGBmZiWcGMzMrIQTg5mZlXBiMDOzEk4MZmZWwonBzMxKODGYmVkJJwYzMyvhxGBmZiXSrOBmZtZ566Dm4Sr5W3RT8n1ArlGksw4YUplLOTGYWeaqbR3w5cuXAzBiyIicI0lhSOX+OzsxmFnmqm3N9d6+3nqVjOvMzKyrZJoYJN0i6Q1Jy4r6jpL0pKRFkuZLOibpl6RGSU2Slkg6OsvYzMysfVmPGG4FTmzT9wPg0og4CpiatAHGASOSrwnADRnHZmZm7cg0MUTEI8Datt3AXsn2+4DXku1TgNui4Elgb0n7ZxmfmZm9Vx4PnycB90u6hkJi+njSPwT4Q9FxzUnfyq4Nz3qTxsZGmpqaMrl268yWLB68Dh8+vOoe6Fr1yOPh8znAtyLiAOBbwM1Jv9o5Ntq7gKQJyfOJ+atWrcooTLOd079/f/r37593GGap5TFiaAC+mWzfCfwk2W4GDig6bijv3mYqEREzgBkA9fX17SYPs3L4r26z98pjxPAa8OlkewywPNmeDXwtmZ30UWB9RPg2klWt22+/nWOPPZZZs2blHYpZKllPV50FPAEcIqlZ0lnA2cB0SYuBKynMQAKYA7wMNAE3AedmGZtZ1m666SYAbrjBE+ysumR6Kykixnewa1Q7xwZwXpbxmHWV22+/vaQ9a9Ysxo/v6H8Hs+7Fbz6bZaB1tNDKowarJk4MZmZWwonBzMxKODGYZeDss88uaZ9zzjk5RWKWnhODWQbOPPPMkrYfPFs1cWIwy8jee+9d8t2sWjgxmGXgxRdfZN26dQCsW7cus3pMZlnwCm5mGbj88stL2tOmTeO2227LKZqeK6siiFkWQITuXwTRicEsAytWrNhu27q33l780InBLAN1dXUlyaCuri63WHqy7vxXdzXzMwazDEyePLmkPXXq1JwiMUvPicEsAyNHjixpDx8+PKdIzNJzYjDLwG9+85uS9kMPPZRTJGbpOTGYZeDKK68saV922WU5RWKWnhODWQa2bt263bZZd+bEYJaBPn36bLdt1p05MZhl4Hvf+15Je8qUKTlFYpaeE4NZBo4//vg/jRL69OnDcccdl3NEZuVzYjDLyJFHHgnAUUcdlXMkZuk4MZhlYPXq1SxduhSApUuXsmbNmpwjMiufn4hVQDUW8uruRbyq3cyZM4kIALZt28bMmTO54IILco7KrDweMXRj/fv37/XFvKrVvHnzaGlpAaClpYUHHngg54jMyucRQwX4L29ra+zYscyZM4eWlhb69u3LCSeckHdIZmXziMEsAw0NDUgCoKamhoaGhpwjMitfpolB0i2S3pC0rKjvF5IWJV8rJC1K+uskbS7ad2OWsZllqba2lnHjxiGJcePGMWjQoLxDMitb1reSbgWuB/60dFVEfKV1W9J0YH3R8S9FhOf2WY/Q0NDAihUrPFqwqpNpYoiIRyTVtbdPhXH26cCYLGMwy0ttbS3XXXdd3mGYpZbnM4ZPAa9HxPKivoMkLZT0W0mfyiswM7PeLM9ZSeOBWUXtlcCwiFgjaRTwK0mHRcSGtidKmgBMABg2bFiXBGtm1lvkMmKQ1Af4MvCL1r6I2BIRa5LtBcBLwMj2zo+IGRFRHxH1gwcP7oqQzcx6jbxuJR0PvBARza0dkgZL2iXZPhgYAbycU3xmZr1WpreSJM0CRgO1kpqBiyPiZuAMSm8jARwLTJO0FXgH+EZErN3Rz1iwYMFqSb+rbOTdSi2wOu8grNP8+VWvnv7ZHdjRDrXWc7HuSdL8iKjPOw7rHH9+1as3f3Z+89nMzEo4MZiZWQknhu5vRt4B2E7x51e9eu1n52cMZmZWwiMGMzMr4cRgZmYlnBjMrFdKSv0v2/GRvY8Tg1kOkrIwZt2SE0OOJF0t6dyi9iWSLpb0oKRnJC2VdEqyr07S85JukvSspAckeUHobkDS1yQtkbRY0u2SviDpqaRS8G8k7Zscd4mkGZIeoGiNEstVH0kzk8/vl5J2TxYQqwWQVC/p4WT700ULiS2UtGeukWfIiSFfPwe+UtQ+Hfgp8KWIOBo4Dpiu1jUiC/Wj/jkiDgPWAX/ZlcHae0k6DPg+MCYijgS+CTwGfDQiPkLhM/6HolNGAadExFe7PFhrzyHAjIg4AtgAnLudYy8EzksWE/sUsLkL4suFh7M5ioiFkvaR9GfAYOCPFMqP/5OkY4FtwBBg3+SUVyJiUbK9AKjr4pDtvcYAv4yI1QARsVbS4cAvJO0P7Aq8UnT87Ijosf+gVKE/RMTjyfa/ARO3c+zjwI8k3QHcXVwEtKfxiCF/vwROpTBy+DnwVxSSxKjkL5PXgX7JsVuKznsHJ/buQEDbl4GuA66PiMOBv+Pdzw/gf7oqMCtL288ugK28+2/jnz67iLgK+FugP/CkpA92SYQ5cGLI388pVJs9lUKSeB/wRkS0SDqO7VRAtG7hQeB0SYMAJA2k8Bm+muz3gs/d2zBJH0u2x1O4DbiCwi0/KLpdK+kDEbE0Iq4G5gNODJaNiHgW2BN4NSJWAncA9ZLmUxg9vJBnfLZ9yed3BfBbSYuBHwGXAHdKepSeXba5J3geaJC0BBgI3ABcClybfH7vFB07SdKy5HPeDMzt8mi7iEtimJlZCY8YzMyshBODmZmVcGIwM7MSTgxmZlbCicHMzEo4MZjtpKQG0oV5x2FWKU4MZmZWwonBLKW21VTb7Dtb0n8m++6StHvSf1rry1GSHkn6DpP0dFKtc4mkEXn8PmZt+QU3sxSSaqp3A5+IiNVJCYyJwKaIuEbSoIhYkxx7OfB6RFwnaSlwYkS8KmnviFgn6TrgyYi4Q9KuwC4usGfdgUcMZum8p5pqm/0flvRokgj+Cjgs6X8cuFXS2cAuSd8TwPckfRc40EnBugsnBrN02qumWuxW4O+TyqqXklTnjIhvAJOBA4BFycjiZ8DJFOru3C9pTJaBm5XLicEsnfaqqRbbE1gpqS+FEQPJcR+IiKciYiqFwnoHSDoYeDkiGoHZwBFd8huY7YDr+ZulEBHPSmqtpvoOsJBCmeZWU4CngN8BSykkCoAfJg+XRSG5LAYuAv5aUgvw38C0LvklzHbAD5/NzKyEbyWZmVkJJwYzMyvhxGBmZiWcGMzMrIQTg5mZlXBiMDOzEk4MZmZW4n8B58gujeNofd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-skewness_about.2 vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['skewness_about.2'])\n",
    "#Inference: From below graph, it is evident that skewness_about.2 is almost overlapping for each class\n",
    "#bus has wide spread range w.r.t skewness_about.2\n",
    "#car has outliers w.r.t skewness_about.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3974,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Among the variables-skewness_about,skewness_about.1,skewness_about.2, only first 2 variables shows similarity w.r.t \n",
    "#target variable 'class'. I think we can consider one of the column for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3975,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([197, 199, 196, 207, 183, 204, 202, 208, 195, 194, 185, 193, 192,\n",
       "       206, 201, 205, 200, 189, 182, 209, 184, 187, 188, 198, 191, 190,\n",
       "       203, 186, 210, 211, 181])"
      ]
     },
     "execution_count": 3975,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['hollows_ratio'].unique()\n",
    "#No 'nan' values present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3976,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa299fa10>"
      ]
     },
     "execution_count": 3976,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vehicles[\"hollows_ratio\"],rug=True)\n",
    "#Data is somewhat symmertically spread across variable hollows_ratio.\n",
    "#Has multiple modes and concentration can be found between value range-181-201 approximately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3977,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa2b14e50>"
      ]
     },
     "execution_count": 3977,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(vehicles[\"hollows_ratio\"])\n",
    "#No outliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3978,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa2bf6ad0>"
      ]
     },
     "execution_count": 3978,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis-hollows_ratio vs class\n",
    "sns.boxplot(vehicles['class'],vehicles['hollows_ratio'])\n",
    "#Inference: From below graph, it is evident that hollows_ratio is almost overlapping between classes-van and car\n",
    "#bus has wide spread range w.r.t hollows_ratio and slightly right skewed\n",
    "#car has outliers w.r.t hollows_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3979,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa2cf4ed0>"
      ]
     },
     "execution_count": 3979,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis of categorical variable-'class'\n",
    "sns.countplot(vehicles[\"class\"])\n",
    "#From below graph we can conclude that class of type-car has highest presence in data set and \n",
    "#remainaing class types-van and bus shares equal presence in data set (second highest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3980,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's print heatmap and re-confirm that all the nan/null values are replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3981,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa2dc97d0>"
      ]
     },
     "execution_count": 3981,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(vehicles.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "#Inference: null values are present under columns are replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3982,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['van', 'car', 'bus'], dtype=object)"
      ]
     },
     "execution_count": 3982,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['class'].unique()\n",
    "#The data set has type of vehicle already mentioned (Target variable) . \n",
    "#This is categorical variable which needs to be converted into integer form\n",
    "#Also we need to drop it while performing unsupervised learning as this data already has clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3983,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car    429\n",
       "bus    218\n",
       "van    199\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 3983,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(vehicles['class'])\n",
    "#So from below data, we can see that total 846 values present and hence no missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3984,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3])"
      ]
     },
     "execution_count": 3984,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Labelling the variable based on size of the vehicle\n",
    "cleanup_nums = { \"class\":     {\"car\": 1, \"van\": 2 ,\"bus\": 3}                }\n",
    "#Replacing in the data set\n",
    "vehicles.replace(cleanup_nums, inplace=True)\n",
    "vehicles['class'].unique()\n",
    "#Inference: Labelling and replacement is reflecting successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3985,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data clean up and analysis is completed. We are good to proceed further.\n",
    "#Overall inference from the EDA is summarized below:\n",
    "#Presence of nan values taken care\n",
    "#Outliers treatment taken care\n",
    "#Some of the variables-scaled_variance and scaled_variance.1 display similar behavious w.r.t 'class'\n",
    "#Also skewness_about and skewness_about.1 display similar behavious w.r.t 'class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3986,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into train and test \n",
    "#(Suggestion: specify “random state” if you are using train_test_split from Sklearn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3987,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     compactness  circularity  distance_circularity  radius_ratio  \\\n",
       " 775           97         38.0                  75.0         188.0   \n",
       " 242           85         42.0                  59.0         132.0   \n",
       " 301           93         43.0                  78.0         166.0   \n",
       " 170          108         51.0                 103.0         197.0   \n",
       " 187           98         45.0                  76.0         166.0   \n",
       " ..           ...          ...                   ...           ...   \n",
       " 835          111         58.0                 105.0         183.0   \n",
       " 192           93         43.0                  76.0         149.0   \n",
       " 629           90         42.0                  63.0         144.0   \n",
       " 559          101         56.0                 101.0         231.0   \n",
       " 684           99         38.0                  74.0         184.0   \n",
       " \n",
       "      pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       " 775                  68.0                        6          171.0   \n",
       " 242                  58.0                        7          149.0   \n",
       " 301                  59.0                        7          151.0   \n",
       " 170                  60.0                       11          211.0   \n",
       " 187                  60.0                        7          157.0   \n",
       " ..                    ...                      ...            ...   \n",
       " 835                  51.0                        6          265.0   \n",
       " 192                  57.0                        7          149.0   \n",
       " 629                  59.0                        7          131.0   \n",
       " 559                  72.0                       10          217.0   \n",
       " 684                  66.0                        6          164.0   \n",
       " \n",
       "      elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       " 775           37.0                    20.0                        129   \n",
       " 242           46.0                    19.0                        144   \n",
       " 301           44.0                    19.0                        141   \n",
       " 170           31.0                    24.0                        160   \n",
       " 187           42.0                    20.0                        148   \n",
       " ..             ...                     ...                        ...   \n",
       " 835           26.0                    29.0                        174   \n",
       " 192           44.0                    19.0                        143   \n",
       " 629           50.0                    18.0                        142   \n",
       " 559           31.0                    24.0                        171   \n",
       " 684           39.0                    20.0                        131   \n",
       " \n",
       "      scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       " 775            199.0              450.0                      137.0   \n",
       " 242            166.0              320.0                      172.0   \n",
       " 301            182.0              342.0                      174.0   \n",
       " 170            222.0              661.0                      187.0   \n",
       " 187            184.0              371.0                      186.0   \n",
       " ..               ...                ...                        ...   \n",
       " 835            285.0              363.5                      255.0   \n",
       " 192            172.0              335.0                      176.0   \n",
       " 629            154.0              259.0                      162.0   \n",
       " 559            232.0              698.0                      216.0   \n",
       " 684            193.0              414.0                      137.0   \n",
       " \n",
       "      scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       " 775                        74.00             2.0               6.0   \n",
       " 242                        83.00             8.0               4.0   \n",
       " 301                        68.00            15.0               2.0   \n",
       " 170                        67.00             7.0               3.0   \n",
       " 187                        69.00            13.0              10.0   \n",
       " ..                           ...             ...               ...   \n",
       " 835                        85.00             4.0               8.0   \n",
       " 192                        71.25            14.0               0.0   \n",
       " 629                        65.00            15.0               3.0   \n",
       " 559                        72.00             2.0               6.0   \n",
       " 684                        71.00             2.0              22.0   \n",
       " \n",
       "      skewness_about.2  hollows_ratio  \n",
       " 775             197.0            199  \n",
       " 242             179.0            182  \n",
       " 301             193.0            197  \n",
       " 170             190.0            200  \n",
       " 187             190.0            196  \n",
       " ..                ...            ...  \n",
       " 835             181.0            183  \n",
       " 192             189.0            194  \n",
       " 629             197.0            204  \n",
       " 559             187.0            197  \n",
       " 684             200.0            202  \n",
       " \n",
       " [592 rows x 18 columns],\n",
       "      compactness  circularity  distance_circularity  radius_ratio  \\\n",
       " 506          101         51.0                 112.0         201.0   \n",
       " 222          100         50.0                  81.0         197.0   \n",
       " 612           80         34.0                  42.0         110.0   \n",
       " 730          101         55.0                 107.0         200.0   \n",
       " 661          110         46.0                 100.0         197.0   \n",
       " ..           ...          ...                   ...           ...   \n",
       " 362           95         47.0                  81.0         176.0   \n",
       " 500          101         56.0                 100.0         204.0   \n",
       " 424           91         41.0                  93.0         197.0   \n",
       " 487           93         45.0                  81.0         177.0   \n",
       " 358           87         45.0                  82.0         164.0   \n",
       " \n",
       "      pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       " 506                  59.0                       11          214.0   \n",
       " 222                  61.0                        6          186.0   \n",
       " 612                  57.0                        3          114.0   \n",
       " 730                  61.0                       11          225.0   \n",
       " 661                  61.0                        9          193.0   \n",
       " ..                    ...                      ...            ...   \n",
       " 362                  59.0                        7          168.0   \n",
       " 500                  62.0                       12          227.0   \n",
       " 424                  65.0                        9          183.0   \n",
       " 487                  64.0                        7          160.0   \n",
       " 358                  60.0                        8          156.0   \n",
       " \n",
       "      elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       " 506           32.0                    24.0                        162   \n",
       " 222           34.0                    22.0                        158   \n",
       " 612           59.0                    17.0                        119   \n",
       " 730           30.0                    25.0                        178   \n",
       " 661           34.0                    22.0                        149   \n",
       " ..             ...                     ...                        ...   \n",
       " 362           39.0                    20.0                        152   \n",
       " 500           30.0                    25.0                        178   \n",
       " 424           36.0                    21.0                        137   \n",
       " 487           41.0                    20.0                        147   \n",
       " 358           42.0                    19.0                        144   \n",
       " \n",
       "      scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       " 506            223.0              667.0                      194.0   \n",
       " 222            206.0              531.0                      198.0   \n",
       " 612            131.0              191.0                      121.0   \n",
       " 730            228.0              730.0                      204.0   \n",
       " 661            209.0              561.0                      160.0   \n",
       " ..               ...                ...                        ...   \n",
       " 362            196.0              425.0                      185.0   \n",
       " 500            231.0              757.0                      204.0   \n",
       " 424            202.0              504.0                      153.0   \n",
       " 487            180.0              383.0                      188.0   \n",
       " 358            181.0              366.0                      174.0   \n",
       " \n",
       "      scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       " 506                         65.0             0.0              36.0   \n",
       " 222                         74.0             6.0               1.0   \n",
       " 612                         87.0             4.0               7.0   \n",
       " 730                         74.0             8.0              35.0   \n",
       " 661                         65.0            11.0               7.0   \n",
       " ..                           ...             ...               ...   \n",
       " 362                         67.0             4.0               4.0   \n",
       " 500                         73.0             0.0              11.0   \n",
       " 424                         66.0            11.0              24.0   \n",
       " 487                         70.0            11.0              11.0   \n",
       " 358                         70.0             2.0               2.0   \n",
       " \n",
       "      skewness_about.2  hollows_ratio  \n",
       " 506             190.0            206  \n",
       " 222             197.0            198  \n",
       " 612             179.0            183  \n",
       " 730             187.0            201  \n",
       " 661             194.0            203  \n",
       " ..                ...            ...  \n",
       " 362             191.0            198  \n",
       " 500             186.0            197  \n",
       " 424             193.0            200  \n",
       " 487             192.0            199  \n",
       " 358             190.0            196  \n",
       " \n",
       " [254 rows x 18 columns],\n",
       " 775    3\n",
       " 242    3\n",
       " 301    1\n",
       " 170    1\n",
       " 187    1\n",
       "       ..\n",
       " 835    3\n",
       " 192    1\n",
       " 629    2\n",
       " 559    1\n",
       " 684    3\n",
       " Name: class, Length: 592, dtype: int64,\n",
       " 506    1\n",
       " 222    3\n",
       " 612    1\n",
       " 730    1\n",
       " 661    1\n",
       "       ..\n",
       " 362    1\n",
       " 500    1\n",
       " 424    1\n",
       " 487    1\n",
       " 358    1\n",
       " Name: class, Length: 254, dtype: int64)"
      ]
     },
     "execution_count": 3987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the data into train and test \n",
    "#(Suggestion: specify “random state” if you are using train_test_split from Sklearn)\n",
    "\n",
    "#Importing required libaries\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "#Drop the target 'class' from data\n",
    "x=vehicles.drop('class',axis=1)\n",
    "y=vehicles['class']\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.70,test_size=0.30,random_state=0)\n",
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3988,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do data Scaling/Normalization if required\n",
    "#This is needed we have variables in different scale ex: scaled_variance.1 etc\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train= sc.fit_transform(x_train)\n",
    "x_test = sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3989,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.98\n",
      "Accuracy on test set: 0.97\n"
     ]
    }
   ],
   "source": [
    "#Train a Support vector machine using the train set and get the accuracy on the test set\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC(gamma='auto')\n",
    "svc.fit(x_train, y_train)\n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(x_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(x_test, y_test)))\n",
    "#Inference: We could see 98% accuracy on train set and 97% accuracy on test set using SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3990,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use PCA from Scikit learn, extract Principal Components that capture about 95% of the variance in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3991,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance Matrix \n",
      "%s [[ 1.00169205  0.68000646  0.78156566  0.72984036  0.22807225  0.48553152\n",
      "   0.80894497 -0.78634991  0.80872907  0.67513298  0.76343925  0.80852914\n",
      "   0.58416325 -0.25182793  0.19946184  0.14713031  0.32162852  0.37577561]\n",
      " [ 0.68000646  1.00169205  0.7918571   0.63899924  0.21602145  0.5588565\n",
      "   0.85087555 -0.82187596  0.8448786   0.9613981   0.80073788  0.83541333\n",
      "   0.9319483   0.06198946  0.13085597 -0.03734851 -0.09146858  0.0584263 ]\n",
      " [ 0.78156566  0.7918571   1.00169205  0.80054422  0.26997793  0.66466763\n",
      "   0.9068752  -0.91001885  0.89545283  0.77163057  0.86470217  0.88778588\n",
      "   0.71376714 -0.23474884  0.10782172  0.24007377  0.15054941  0.33693755]\n",
      " [ 0.72984036  0.63899924  0.80054422  1.00169205  0.67128689  0.45254435\n",
      "   0.78033035 -0.83550076  0.75324953  0.57554792  0.79304415  0.77367321\n",
      "   0.56216077 -0.38623707  0.05880367  0.17594305  0.42728704  0.50452355]\n",
      " [ 0.22807225  0.21602145  0.26997793  0.67128689  1.00169205  0.15603032\n",
      "   0.22625769 -0.33523217  0.19217743  0.15465035  0.23946765  0.22603859\n",
      "   0.1687586  -0.31959533 -0.03769707 -0.01229116  0.43347343  0.43949713]\n",
      " [ 0.48553152  0.5588565   0.66466763  0.45254435  0.15603032  1.00169205\n",
      "   0.48078389 -0.49367466  0.47939399  0.6415078   0.38823965  0.4515376\n",
      "   0.41246554 -0.35017651  0.10196943  0.08751116  0.07853801  0.40676447]\n",
      " [ 0.80894497  0.85087555  0.9068752   0.78033035  0.22625769  0.48078389\n",
      "   1.00169205 -0.97494663  0.99110386  0.80922715  0.96367612  0.98869513\n",
      "   0.80550249  0.01191214  0.0624465   0.19930306  0.02238802  0.13196657]\n",
      " [-0.78634991 -0.82187596 -0.91001885 -0.83550076 -0.33523217 -0.49367466\n",
      "  -0.97494663  1.00169205 -0.94895451 -0.77278391 -0.94802453 -0.95515701\n",
      "  -0.77027165  0.07941916 -0.05215809 -0.174973   -0.13410902 -0.23276304]\n",
      " [ 0.80872907  0.8448786   0.89545283  0.75324953  0.19217743  0.47939399\n",
      "   0.99110386 -0.94895451  1.00169205  0.80969154  0.94867018  0.97945628\n",
      "   0.80017457  0.02409737  0.07275076  0.20771331 -0.00345254  0.11174726]\n",
      " [ 0.67513298  0.9613981   0.77163057  0.57554792  0.15465035  0.6415078\n",
      "   0.80922715 -0.77278391  0.80969154  1.00169205  0.74353627  0.7922127\n",
      "   0.87031692  0.04840359  0.12966605 -0.0331541  -0.09672511  0.08144261]\n",
      " [ 0.76343925  0.80073788  0.86470217  0.79304415  0.23946765  0.38823965\n",
      "   0.96367612 -0.94802453  0.94867018  0.74353627  1.00169205  0.95184322\n",
      "   0.78205654  0.03161096  0.02498622  0.19553182  0.03198475  0.09352406]\n",
      " [ 0.80852914  0.83541333  0.88778588  0.77367321  0.22603859  0.4515376\n",
      "   0.98869513 -0.95515701  0.97945628  0.7922127   0.95184322  1.00169205\n",
      "   0.79267131  0.01051758  0.06581915  0.19985336  0.03302415  0.13022635]\n",
      " [ 0.58416325  0.9319483   0.71376714  0.56216077  0.1687586   0.41246554\n",
      "   0.80550249 -0.77027165  0.80017457  0.87031692  0.78205654  0.79267131\n",
      "   1.00169205  0.20094465  0.16726148 -0.06449626 -0.20317653 -0.08953446]\n",
      " [-0.25182793  0.06198946 -0.23474884 -0.38623707 -0.31959533 -0.35017651\n",
      "   0.01191214  0.07941916  0.02409737  0.04840359  0.03161096  0.01051758\n",
      "   0.20094465  1.00169205 -0.09913336 -0.09213506 -0.82654223 -0.90514746]\n",
      " [ 0.19946184  0.13085597  0.10782172  0.05880367 -0.03769707  0.10196943\n",
      "   0.0624465  -0.05215809  0.07275076  0.12966605  0.02498622  0.06581915\n",
      "   0.16726148 -0.09913336  1.00169205 -0.04410244  0.12237202  0.10648599]\n",
      " [ 0.14713031 -0.03734851  0.24007377  0.17594305 -0.01229116  0.08751116\n",
      "   0.19930306 -0.174973    0.20771331 -0.0331541   0.19553182  0.19985336\n",
      "  -0.06449626 -0.09213506 -0.04410244  1.00169205  0.0790537   0.16822246]\n",
      " [ 0.32162852 -0.09146858  0.15054941  0.42728704  0.43347343  0.07853801\n",
      "   0.02238802 -0.13410902 -0.00345254 -0.09672511  0.03198475  0.03302415\n",
      "  -0.20317653 -0.82654223  0.12237202  0.0790537   1.00169205  0.89262061]\n",
      " [ 0.37577561  0.0584263   0.33693755  0.50452355  0.43949713  0.40676447\n",
      "   0.13196657 -0.23276304  0.11174726  0.08144261  0.09352406  0.13022635\n",
      "  -0.08953446 -0.90514746  0.10648599  0.16822246  0.89262061  1.00169205]]\n",
      "Eigenvectors \n",
      "[[-0.27121371 -0.08916909 -0.02908781 -0.1282358   0.12886046  0.2899743\n",
      "  -0.17451304  0.76485809 -0.35047633  0.16150747 -0.08258039 -0.02063201\n",
      "   0.02009984 -0.03598669  0.11810386  0.10427529 -0.04233625  0.02391411]\n",
      " [-0.28633621  0.13530602 -0.20646866  0.07464613 -0.09524073 -0.0629999\n",
      "   0.40352522  0.04324595 -0.06431622 -0.20115865  0.02129187 -0.0794124\n",
      "   0.03058187 -0.06430016  0.26794252 -0.15071946  0.40688759  0.59771401]\n",
      " [-0.30156639 -0.03965481  0.03152561 -0.12462624 -0.1025028  -0.03200999\n",
      "  -0.19218903 -0.26721086 -0.12508576  0.12238689 -0.53307491 -0.64518868\n",
      "   0.00658985  0.12639097 -0.12407308 -0.08939665  0.00459844  0.02454561]\n",
      " [-0.27207477 -0.19520816  0.12120681  0.22646486  0.11883884 -0.13659883\n",
      "  -0.16436101 -0.09833521 -0.27296062 -0.08184     0.4721192  -0.08349983\n",
      "   0.00570424  0.45144224  0.23303147 -0.38680998 -0.00791453 -0.18311321]\n",
      " [-0.10822605 -0.26294486  0.07649976  0.60397343  0.14275032 -0.57473217\n",
      "  -0.07998571  0.19612058  0.02157574 -0.04442375 -0.22942582  0.04818675\n",
      "  -0.01722543 -0.19447102 -0.15375135  0.17588791  0.00354587  0.05342127]\n",
      " [-0.19205958 -0.09983488 -0.27956868 -0.24643022 -0.59013657 -0.33121029\n",
      "  -0.36833476  0.09745787  0.16603132  0.22765328  0.24176815  0.1333305\n",
      "  -0.00799857 -0.14423155 -0.10882536 -0.13138076  0.06393564  0.03807927]\n",
      " [-0.31025191  0.07924501  0.11301132 -0.0228688   0.05887937  0.10252981\n",
      "  -0.09613005 -0.07131686  0.14856087 -0.09376757 -0.09313228  0.19307012\n",
      "  -0.87460252 -0.04052301  0.06259964  0.00387001  0.07264061 -0.05717344]\n",
      " [ 0.30872253 -0.01495261 -0.10930734 -0.04980015 -0.05717263 -0.06807012\n",
      "   0.11239487  0.18092143 -0.31310336 -0.09377987  0.04052258 -0.00341983\n",
      "  -0.27527157  0.39281635 -0.66041224 -0.09706236  0.15245808  0.17146742]\n",
      " [-0.30658086  0.09092877  0.10681004 -0.05436242  0.0555894   0.10745807\n",
      "  -0.0945572  -0.02864937  0.0815274  -0.20157572 -0.15893454  0.31404324\n",
      "   0.33418297  0.0977612  -0.25600422  0.03901176  0.61429552 -0.34951366]\n",
      " [-0.27624133  0.12849534 -0.25019403 -0.00216756 -0.19932096 -0.06839705\n",
      "   0.36390635  0.21907158  0.16424792 -0.45388361  0.10069798 -0.26144898\n",
      "  -0.00454641  0.05627102 -0.11691761  0.15478222 -0.32313691 -0.4037766 ]\n",
      " [-0.29846436  0.08179002  0.17099851  0.03583512  0.11717493  0.13350218\n",
      "  -0.09471927 -0.19387489 -0.01119024  0.11253496  0.50763326 -0.19707125\n",
      "   0.03860343 -0.08374988 -0.32119795  0.55054383  0.00094436  0.2674005 ]\n",
      " [-0.30630827  0.07637686  0.12080765 -0.01961556  0.08542052  0.12195226\n",
      "  -0.08220168 -0.04163241  0.12179908 -0.17989152 -0.14002274  0.35229605\n",
      "   0.20481021  0.04020746 -0.28896882 -0.34927347 -0.53011222  0.36566454]\n",
      " [-0.26410408  0.20565392 -0.19157275  0.10970717  0.03898218 -0.08268654\n",
      "   0.44550159 -0.1674642  -0.27842594  0.62430252 -0.05367947  0.2467028\n",
      "  -0.01330232 -0.00959172 -0.10638382 -0.04228684 -0.10458144 -0.22139735]\n",
      " [ 0.04446863  0.50179092  0.11379927  0.11248268  0.16147444 -0.13387315\n",
      "  -0.06059956  0.3179747   0.53822396  0.31822493  0.03612891 -0.18894443\n",
      "   0.01544519  0.34884854  0.01057411 -0.11736933  0.07075975  0.05535301]\n",
      " [-0.03854286 -0.04589949 -0.53514997 -0.38768427  0.66115393 -0.29041155\n",
      "  -0.13325607 -0.09626966  0.0616065  -0.07076005  0.02708273  0.00501087\n",
      "  -0.00114811 -0.00329436  0.0043055   0.03578398 -0.00334581  0.01874473]\n",
      " [-0.05230828 -0.07938903  0.61152354 -0.5431589   0.05795312 -0.44213136\n",
      "   0.31593675  0.108802   -0.05386225  0.00536784  0.04206012 -0.00206081\n",
      "   0.00332689 -0.07603137  0.02064165 -0.02025392 -0.00754541 -0.00988215]\n",
      " [-0.04527465 -0.50309811 -0.00927348  0.04449904  0.1748827   0.28298986\n",
      "   0.25908964  0.0826354   0.36206708  0.18256702  0.14939403 -0.22326249\n",
      "  -0.03629565 -0.2818327  -0.26053264 -0.38497377  0.13736315 -0.07659848]\n",
      " [-0.09008533 -0.50551478 -0.06469766 -0.07767203 -0.10869257  0.05930327\n",
      "   0.18675452 -0.0324051   0.28488667  0.13904046 -0.15017303  0.19045868\n",
      "   0.02526739  0.57607655  0.15292096  0.36760558 -0.04762988  0.14917571]]\n"
     ]
    }
   ],
   "source": [
    "# generating the covariance matrix and the eigen values for the PCA analysis\n",
    "cov_matrix = np.cov(x_train.T) # the relevanat covariance matrix\n",
    "print('Covariance Matrix \\n%s', cov_matrix)\n",
    "\n",
    "#generating the eigen values and the eigen vectors\n",
    "e_vals, e_vecs = np.linalg.eig(cov_matrix)\n",
    "print('Eigenvectors \\n%s' %e_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3992,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eigenvalues \n",
      "[9.77663713e+00 3.33869552e+00 1.24777428e+00 1.10218942e+00\n",
      " 9.03091944e-01 6.36166185e-01 3.11966572e-01 2.39787146e-01\n",
      " 1.23902420e-01 7.69019265e-02 7.15620369e-02 6.11113643e-02\n",
      " 3.35317156e-03 3.90603167e-02 3.03961502e-02 2.73516838e-02\n",
      " 1.97310009e-02 2.07785875e-02]\n"
     ]
    }
   ],
   "source": [
    "print('\\nEigenvalues \\n%s' %e_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3993,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative Variance Explained [ 54.22290299  72.7398798   79.66024955  85.77318076  90.78188326\n",
      "  94.31016982  96.04038982  97.37029038  98.05747441  98.48398566\n",
      "  98.88088096  99.21981505  99.43645025  99.6050325   99.75672963\n",
      "  99.87197123  99.98140274 100.        ]\n"
     ]
    }
   ],
   "source": [
    "# \"cumulative variance explained\" analysis \n",
    "tot = sum(e_vals)\n",
    "var_exp = [( i /tot ) * 100 for i in sorted(e_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "print(\"Cumulative Variance Explained\", cum_var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3994,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the variance expalained by the principal components and the cumulative variance explained.\n",
    "plt.figure(figsize=(10 , 5))\n",
    "plt.bar(range(1, e_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')\n",
    "plt.step(range(1, e_vals.size + 1), cum_var_exp, where='mid', label = 'Cumulative explained variance')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.xlabel('Principal Components')\n",
    "plt.legend(loc = 'best')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#Inference: From below graph we can learn that ~95% data variance can be captured \n",
    "#when we take only ~ 6 variables from the original data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3995,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.27486377 -0.28862844 -0.303811   -0.2679999  -0.07367439 -0.20542201\n",
      "  -0.31012847  0.30888772 -0.30776124 -0.28208938 -0.30099055 -0.30262895\n",
      "  -0.2603852   0.03898709 -0.03292546 -0.07128107 -0.02230566 -0.07312317]\n",
      " [-0.07930426  0.12112866 -0.05894321 -0.20439674 -0.24064605 -0.1345645\n",
      "   0.07055325 -0.01228618  0.08336366  0.10374901  0.07334559  0.06469688\n",
      "   0.22066013  0.50831978  0.0465361  -0.11595374 -0.49280113 -0.50972265]\n",
      " [-0.10785641  0.10185303 -0.1073343   0.23726752  0.62170817 -0.21043234\n",
      "  -0.04337193 -0.02816896 -0.05187363  0.03258923  0.01264508 -0.03051392\n",
      "   0.16335383  0.0601597  -0.23389205 -0.61504609  0.04347226 -0.08169689]\n",
      " [ 0.15931149  0.11990208 -0.03732425 -0.08280713 -0.11229657  0.00094766\n",
      "  -0.06464904  0.07236423 -0.0552066   0.11003301 -0.09535256 -0.06273043\n",
      "   0.11911933 -0.08518099  0.82164658 -0.42007724  0.1593486   0.05812312]\n",
      " [ 0.19264492 -0.2441761  -0.0299199   0.12014141 -0.11276708 -0.70318025\n",
      "   0.16109835 -0.14705894  0.15861496 -0.36112877  0.2335739   0.18445379\n",
      "  -0.09325179  0.03793168  0.11166884  0.02778123  0.24013286 -0.08936464]\n",
      " [-0.14992896 -0.01086539 -0.01594097  0.20215211  0.57873146 -0.06666148\n",
      "  -0.02507946  0.03591544 -0.04118348 -0.0540829  -0.04702283 -0.03484595\n",
      "   0.00088395  0.13678284  0.46256489  0.55181404 -0.19834427 -0.10212097]]\n",
      "[0.54126013 0.19087598 0.06698305 0.06106829 0.04448527 0.04039124]\n"
     ]
    }
   ],
   "source": [
    "#Proceeding with 6 components\n",
    "pca_new = PCA(n_components=6)\n",
    "#Fitting PCA into train data\n",
    "pca_new.fit(x_train)\n",
    "#Fitting PCA into test data\n",
    "pca_new.fit(x_test)\n",
    "print(pca_new.components_)\n",
    "print(pca_new.explained_variance_ratio_)\n",
    "Xpca_train_new = pca_new.transform(x_train)\n",
    "Xpca_test_new = pca_new.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3996,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original shape:    (254, 18)\n",
      "transformed shape: (254, 6)\n"
     ]
    }
   ],
   "source": [
    "print(\"original shape:   \", x_test.shape)\n",
    "print(\"transformed shape:\", Xpca_test_new.shape)\n",
    "#Inference: From below data it is evident that dimension of the data set is reduced from 18 to 6 \n",
    "#(excluding target variable-class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3997,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repeat steps 3,4 and 5 but this time, use Principal Components instead of the original data. \n",
    "#And the accuracy score should be on the same rows of test data that were used earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3998,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 3998,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying SVM on principal components derived above i.e., 6\n",
    "clf = SVC(gamma='auto')\n",
    "clf.fit(Xpca_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3999,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before PCA score of train set:  0.9780405405405406\n",
      "Before PCA score of test set:  0.9724409448818898\n",
      "After PCA score of train set:  0.9543918918918919\n",
      "After PCA score of test set:  0.9251968503937008\n"
     ]
    }
   ],
   "source": [
    "#Compare the accuracy scores and cross validation scores of Support vector machines \n",
    "#– one trained using raw data and the other using Principal Components, and mention your findings\n",
    "\n",
    "clf = SVC(gamma='auto')\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "print ('Before PCA score of train set: ', clf.score(x_train, y_train))\n",
    "\n",
    "print ('Before PCA score of test set: ', clf.score(x_test, y_test))\n",
    "\n",
    "clf.fit(Xpca_train_new, y_train)\n",
    "\n",
    "print ('After PCA score of train set: ', clf.score(Xpca_train_new, y_train))\n",
    "\n",
    "print ('After PCA score of test set: ', clf.score(Xpca_test_new, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3829,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conclusion:\n",
    "#With orignal data we could build a model which displays accuracy of ~98%(on train data) and ~97% (on test data)with 18 columns\n",
    "#Post PCA analysis, we could build a model which displays accuracy of ~95% (on train data) and ~92% (on test data) with 6 columns\n",
    "# So the second model seems to be cost-effective as accuracy is not dropped much where as dimension is reduced considerably\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}