{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction To The Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.options.display.max_columns = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', \n",
    "        'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', \n",
    "        'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-rate', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
    "cars = pd.read_csv('imports-85.data', names=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.00</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.00</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>177.3</td>\n",
       "      <td>66.3</td>\n",
       "      <td>53.1</td>\n",
       "      <td>2507</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2844</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>17710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2954</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3086</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>140</td>\n",
       "      <td>5500</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3053</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>7.00</td>\n",
       "      <td>160</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2710</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>20970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2765</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>21105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3055</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>164</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>24565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3230</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>30760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.5</td>\n",
       "      <td>193.8</td>\n",
       "      <td>67.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>3380</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>41315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>3505</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>209</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>36880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.4</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>53.2</td>\n",
       "      <td>1488</td>\n",
       "      <td>l</td>\n",
       "      <td>three</td>\n",
       "      <td>61</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.50</td>\n",
       "      <td>48</td>\n",
       "      <td>5100</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>5151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>155.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1874</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>158.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1909</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.41</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>2128</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>98</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>7957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1967</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>90</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>7609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>?</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>2191</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>98</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1</td>\n",
       "      <td>110</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>103.3</td>\n",
       "      <td>174.6</td>\n",
       "      <td>64.6</td>\n",
       "      <td>59.8</td>\n",
       "      <td>2535</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>122</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>8.50</td>\n",
       "      <td>88</td>\n",
       "      <td>5000</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.9</td>\n",
       "      <td>173.2</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>2811</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>156</td>\n",
       "      <td>mfi</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.90</td>\n",
       "      <td>7.00</td>\n",
       "      <td>145</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2414</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2414</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>10898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-1</td>\n",
       "      <td>65</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2458</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>122</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>11248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>3</td>\n",
       "      <td>197</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2976</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>16558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</td>\n",
       "      <td>197</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3016</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-1</td>\n",
       "      <td>90</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3131</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>171</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>15690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-1</td>\n",
       "      <td>?</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3151</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>161</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2261</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2209</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2264</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2212</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2275</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2319</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>9495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2300</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>100</td>\n",
       "      <td>5500</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>159.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2254</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>165.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>51.4</td>\n",
       "      <td>2221</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2661</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>13295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2579</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>13845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>100.4</td>\n",
       "      <td>183.1</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2563</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>88</td>\n",
       "      <td>5500</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>12290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2912</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>12940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3034</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>13415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2935</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>15985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3042</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2</td>\n",
       "      <td>103</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3045</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</td>\n",
       "      <td>74</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3157</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.70</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.80</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses         make fuel-type aspiration  \\\n",
       "0            3                 ?  alfa-romero       gas        std   \n",
       "1            3                 ?  alfa-romero       gas        std   \n",
       "2            1                 ?  alfa-romero       gas        std   \n",
       "3            2               164         audi       gas        std   \n",
       "4            2               164         audi       gas        std   \n",
       "5            2                 ?         audi       gas        std   \n",
       "6            1               158         audi       gas        std   \n",
       "7            1                 ?         audi       gas        std   \n",
       "8            1               158         audi       gas      turbo   \n",
       "9            0                 ?         audi       gas      turbo   \n",
       "10           2               192          bmw       gas        std   \n",
       "11           0               192          bmw       gas        std   \n",
       "12           0               188          bmw       gas        std   \n",
       "13           0               188          bmw       gas        std   \n",
       "14           1                 ?          bmw       gas        std   \n",
       "15           0                 ?          bmw       gas        std   \n",
       "16           0                 ?          bmw       gas        std   \n",
       "17           0                 ?          bmw       gas        std   \n",
       "18           2               121    chevrolet       gas        std   \n",
       "19           1                98    chevrolet       gas        std   \n",
       "20           0                81    chevrolet       gas        std   \n",
       "21           1               118        dodge       gas        std   \n",
       "22           1               118        dodge       gas        std   \n",
       "23           1               118        dodge       gas      turbo   \n",
       "24           1               148        dodge       gas        std   \n",
       "25           1               148        dodge       gas        std   \n",
       "26           1               148        dodge       gas        std   \n",
       "27           1               148        dodge       gas      turbo   \n",
       "28          -1               110        dodge       gas        std   \n",
       "29           3               145        dodge       gas      turbo   \n",
       "..         ...               ...          ...       ...        ...   \n",
       "175         -1                65       toyota       gas        std   \n",
       "176         -1                65       toyota       gas        std   \n",
       "177         -1                65       toyota       gas        std   \n",
       "178          3               197       toyota       gas        std   \n",
       "179          3               197       toyota       gas        std   \n",
       "180         -1                90       toyota       gas        std   \n",
       "181         -1                 ?       toyota       gas        std   \n",
       "182          2               122   volkswagen    diesel        std   \n",
       "183          2               122   volkswagen       gas        std   \n",
       "184          2                94   volkswagen    diesel        std   \n",
       "185          2                94   volkswagen       gas        std   \n",
       "186          2                94   volkswagen       gas        std   \n",
       "187          2                94   volkswagen    diesel      turbo   \n",
       "188          2                94   volkswagen       gas        std   \n",
       "189          3                 ?   volkswagen       gas        std   \n",
       "190          3               256   volkswagen       gas        std   \n",
       "191          0                 ?   volkswagen       gas        std   \n",
       "192          0                 ?   volkswagen    diesel      turbo   \n",
       "193          0                 ?   volkswagen       gas        std   \n",
       "194         -2               103        volvo       gas        std   \n",
       "195         -1                74        volvo       gas        std   \n",
       "196         -2               103        volvo       gas        std   \n",
       "197         -1                74        volvo       gas        std   \n",
       "198         -2               103        volvo       gas      turbo   \n",
       "199         -1                74        volvo       gas      turbo   \n",
       "200         -1                95        volvo       gas        std   \n",
       "201         -1                95        volvo       gas      turbo   \n",
       "202         -1                95        volvo       gas        std   \n",
       "203         -1                95        volvo    diesel      turbo   \n",
       "204         -1                95        volvo       gas      turbo   \n",
       "\n",
       "    num-of-doors   body-style drive-wheels engine-location  wheel-base  \\\n",
       "0            two  convertible          rwd           front        88.6   \n",
       "1            two  convertible          rwd           front        88.6   \n",
       "2            two    hatchback          rwd           front        94.5   \n",
       "3           four        sedan          fwd           front        99.8   \n",
       "4           four        sedan          4wd           front        99.4   \n",
       "5            two        sedan          fwd           front        99.8   \n",
       "6           four        sedan          fwd           front       105.8   \n",
       "7           four        wagon          fwd           front       105.8   \n",
       "8           four        sedan          fwd           front       105.8   \n",
       "9            two    hatchback          4wd           front        99.5   \n",
       "10           two        sedan          rwd           front       101.2   \n",
       "11          four        sedan          rwd           front       101.2   \n",
       "12           two        sedan          rwd           front       101.2   \n",
       "13          four        sedan          rwd           front       101.2   \n",
       "14          four        sedan          rwd           front       103.5   \n",
       "15          four        sedan          rwd           front       103.5   \n",
       "16           two        sedan          rwd           front       103.5   \n",
       "17          four        sedan          rwd           front       110.0   \n",
       "18           two    hatchback          fwd           front        88.4   \n",
       "19           two    hatchback          fwd           front        94.5   \n",
       "20          four        sedan          fwd           front        94.5   \n",
       "21           two    hatchback          fwd           front        93.7   \n",
       "22           two    hatchback          fwd           front        93.7   \n",
       "23           two    hatchback          fwd           front        93.7   \n",
       "24          four    hatchback          fwd           front        93.7   \n",
       "25          four        sedan          fwd           front        93.7   \n",
       "26          four        sedan          fwd           front        93.7   \n",
       "27             ?        sedan          fwd           front        93.7   \n",
       "28          four        wagon          fwd           front       103.3   \n",
       "29           two    hatchback          fwd           front        95.9   \n",
       "..           ...          ...          ...             ...         ...   \n",
       "175         four    hatchback          fwd           front       102.4   \n",
       "176         four        sedan          fwd           front       102.4   \n",
       "177         four    hatchback          fwd           front       102.4   \n",
       "178          two    hatchback          rwd           front       102.9   \n",
       "179          two    hatchback          rwd           front       102.9   \n",
       "180         four        sedan          rwd           front       104.5   \n",
       "181         four        wagon          rwd           front       104.5   \n",
       "182          two        sedan          fwd           front        97.3   \n",
       "183          two        sedan          fwd           front        97.3   \n",
       "184         four        sedan          fwd           front        97.3   \n",
       "185         four        sedan          fwd           front        97.3   \n",
       "186         four        sedan          fwd           front        97.3   \n",
       "187         four        sedan          fwd           front        97.3   \n",
       "188         four        sedan          fwd           front        97.3   \n",
       "189          two  convertible          fwd           front        94.5   \n",
       "190          two    hatchback          fwd           front        94.5   \n",
       "191         four        sedan          fwd           front       100.4   \n",
       "192         four        sedan          fwd           front       100.4   \n",
       "193         four        wagon          fwd           front       100.4   \n",
       "194         four        sedan          rwd           front       104.3   \n",
       "195         four        wagon          rwd           front       104.3   \n",
       "196         four        sedan          rwd           front       104.3   \n",
       "197         four        wagon          rwd           front       104.3   \n",
       "198         four        sedan          rwd           front       104.3   \n",
       "199         four        wagon          rwd           front       104.3   \n",
       "200         four        sedan          rwd           front       109.1   \n",
       "201         four        sedan          rwd           front       109.1   \n",
       "202         four        sedan          rwd           front       109.1   \n",
       "203         four        sedan          rwd           front       109.1   \n",
       "204         four        sedan          rwd           front       109.1   \n",
       "\n",
       "     length  width  height  curb-weight engine-type num-of-cylinders  \\\n",
       "0     168.8   64.1    48.8         2548        dohc             four   \n",
       "1     168.8   64.1    48.8         2548        dohc             four   \n",
       "2     171.2   65.5    52.4         2823        ohcv              six   \n",
       "3     176.6   66.2    54.3         2337         ohc             four   \n",
       "4     176.6   66.4    54.3         2824         ohc             five   \n",
       "5     177.3   66.3    53.1         2507         ohc             five   \n",
       "6     192.7   71.4    55.7         2844         ohc             five   \n",
       "7     192.7   71.4    55.7         2954         ohc             five   \n",
       "8     192.7   71.4    55.9         3086         ohc             five   \n",
       "9     178.2   67.9    52.0         3053         ohc             five   \n",
       "10    176.8   64.8    54.3         2395         ohc             four   \n",
       "11    176.8   64.8    54.3         2395         ohc             four   \n",
       "12    176.8   64.8    54.3         2710         ohc              six   \n",
       "13    176.8   64.8    54.3         2765         ohc              six   \n",
       "14    189.0   66.9    55.7         3055         ohc              six   \n",
       "15    189.0   66.9    55.7         3230         ohc              six   \n",
       "16    193.8   67.9    53.7         3380         ohc              six   \n",
       "17    197.0   70.9    56.3         3505         ohc              six   \n",
       "18    141.1   60.3    53.2         1488           l            three   \n",
       "19    155.9   63.6    52.0         1874         ohc             four   \n",
       "20    158.8   63.6    52.0         1909         ohc             four   \n",
       "21    157.3   63.8    50.8         1876         ohc             four   \n",
       "22    157.3   63.8    50.8         1876         ohc             four   \n",
       "23    157.3   63.8    50.8         2128         ohc             four   \n",
       "24    157.3   63.8    50.6         1967         ohc             four   \n",
       "25    157.3   63.8    50.6         1989         ohc             four   \n",
       "26    157.3   63.8    50.6         1989         ohc             four   \n",
       "27    157.3   63.8    50.6         2191         ohc             four   \n",
       "28    174.6   64.6    59.8         2535         ohc             four   \n",
       "29    173.2   66.3    50.2         2811         ohc             four   \n",
       "..      ...    ...     ...          ...         ...              ...   \n",
       "175   175.6   66.5    53.9         2414         ohc             four   \n",
       "176   175.6   66.5    54.9         2414         ohc             four   \n",
       "177   175.6   66.5    53.9         2458         ohc             four   \n",
       "178   183.5   67.7    52.0         2976        dohc              six   \n",
       "179   183.5   67.7    52.0         3016        dohc              six   \n",
       "180   187.8   66.5    54.1         3131        dohc              six   \n",
       "181   187.8   66.5    54.1         3151        dohc              six   \n",
       "182   171.7   65.5    55.7         2261         ohc             four   \n",
       "183   171.7   65.5    55.7         2209         ohc             four   \n",
       "184   171.7   65.5    55.7         2264         ohc             four   \n",
       "185   171.7   65.5    55.7         2212         ohc             four   \n",
       "186   171.7   65.5    55.7         2275         ohc             four   \n",
       "187   171.7   65.5    55.7         2319         ohc             four   \n",
       "188   171.7   65.5    55.7         2300         ohc             four   \n",
       "189   159.3   64.2    55.6         2254         ohc             four   \n",
       "190   165.7   64.0    51.4         2221         ohc             four   \n",
       "191   180.2   66.9    55.1         2661         ohc             five   \n",
       "192   180.2   66.9    55.1         2579         ohc             four   \n",
       "193   183.1   66.9    55.1         2563         ohc             four   \n",
       "194   188.8   67.2    56.2         2912         ohc             four   \n",
       "195   188.8   67.2    57.5         3034         ohc             four   \n",
       "196   188.8   67.2    56.2         2935         ohc             four   \n",
       "197   188.8   67.2    57.5         3042         ohc             four   \n",
       "198   188.8   67.2    56.2         3045         ohc             four   \n",
       "199   188.8   67.2    57.5         3157         ohc             four   \n",
       "200   188.8   68.9    55.5         2952         ohc             four   \n",
       "201   188.8   68.8    55.5         3049         ohc             four   \n",
       "202   188.8   68.9    55.5         3012        ohcv              six   \n",
       "203   188.8   68.9    55.5         3217         ohc              six   \n",
       "204   188.8   68.9    55.5         3062         ohc             four   \n",
       "\n",
       "     engine-size fuel-system  bore stroke  compression-rate horsepower  \\\n",
       "0            130        mpfi  3.47   2.68              9.00        111   \n",
       "1            130        mpfi  3.47   2.68              9.00        111   \n",
       "2            152        mpfi  2.68   3.47              9.00        154   \n",
       "3            109        mpfi  3.19   3.40             10.00        102   \n",
       "4            136        mpfi  3.19   3.40              8.00        115   \n",
       "5            136        mpfi  3.19   3.40              8.50        110   \n",
       "6            136        mpfi  3.19   3.40              8.50        110   \n",
       "7            136        mpfi  3.19   3.40              8.50        110   \n",
       "8            131        mpfi  3.13   3.40              8.30        140   \n",
       "9            131        mpfi  3.13   3.40              7.00        160   \n",
       "10           108        mpfi  3.50   2.80              8.80        101   \n",
       "11           108        mpfi  3.50   2.80              8.80        101   \n",
       "12           164        mpfi  3.31   3.19              9.00        121   \n",
       "13           164        mpfi  3.31   3.19              9.00        121   \n",
       "14           164        mpfi  3.31   3.19              9.00        121   \n",
       "15           209        mpfi  3.62   3.39              8.00        182   \n",
       "16           209        mpfi  3.62   3.39              8.00        182   \n",
       "17           209        mpfi  3.62   3.39              8.00        182   \n",
       "18            61        2bbl  2.91   3.03              9.50         48   \n",
       "19            90        2bbl  3.03   3.11              9.60         70   \n",
       "20            90        2bbl  3.03   3.11              9.60         70   \n",
       "21            90        2bbl  2.97   3.23              9.41         68   \n",
       "22            90        2bbl  2.97   3.23              9.40         68   \n",
       "23            98        mpfi  3.03   3.39              7.60        102   \n",
       "24            90        2bbl  2.97   3.23              9.40         68   \n",
       "25            90        2bbl  2.97   3.23              9.40         68   \n",
       "26            90        2bbl  2.97   3.23              9.40         68   \n",
       "27            98        mpfi  3.03   3.39              7.60        102   \n",
       "28           122        2bbl  3.34   3.46              8.50         88   \n",
       "29           156         mfi  3.60   3.90              7.00        145   \n",
       "..           ...         ...   ...    ...               ...        ...   \n",
       "175          122        mpfi  3.31   3.54              8.70         92   \n",
       "176          122        mpfi  3.31   3.54              8.70         92   \n",
       "177          122        mpfi  3.31   3.54              8.70         92   \n",
       "178          171        mpfi  3.27   3.35              9.30        161   \n",
       "179          171        mpfi  3.27   3.35              9.30        161   \n",
       "180          171        mpfi  3.27   3.35              9.20        156   \n",
       "181          161        mpfi  3.27   3.35              9.20        156   \n",
       "182           97         idi  3.01   3.40             23.00         52   \n",
       "183          109        mpfi  3.19   3.40              9.00         85   \n",
       "184           97         idi  3.01   3.40             23.00         52   \n",
       "185          109        mpfi  3.19   3.40              9.00         85   \n",
       "186          109        mpfi  3.19   3.40              9.00         85   \n",
       "187           97         idi  3.01   3.40             23.00         68   \n",
       "188          109        mpfi  3.19   3.40             10.00        100   \n",
       "189          109        mpfi  3.19   3.40              8.50         90   \n",
       "190          109        mpfi  3.19   3.40              8.50         90   \n",
       "191          136        mpfi  3.19   3.40              8.50        110   \n",
       "192           97         idi  3.01   3.40             23.00         68   \n",
       "193          109        mpfi  3.19   3.40              9.00         88   \n",
       "194          141        mpfi  3.78   3.15              9.50        114   \n",
       "195          141        mpfi  3.78   3.15              9.50        114   \n",
       "196          141        mpfi  3.78   3.15              9.50        114   \n",
       "197          141        mpfi  3.78   3.15              9.50        114   \n",
       "198          130        mpfi  3.62   3.15              7.50        162   \n",
       "199          130        mpfi  3.62   3.15              7.50        162   \n",
       "200          141        mpfi  3.78   3.15              9.50        114   \n",
       "201          141        mpfi  3.78   3.15              8.70        160   \n",
       "202          173        mpfi  3.58   2.87              8.80        134   \n",
       "203          145         idi  3.01   3.40             23.00        106   \n",
       "204          141        mpfi  3.78   3.15              9.50        114   \n",
       "\n",
       "    peak-rpm  city-mpg  highway-mpg  price  \n",
       "0       5000        21           27  13495  \n",
       "1       5000        21           27  16500  \n",
       "2       5000        19           26  16500  \n",
       "3       5500        24           30  13950  \n",
       "4       5500        18           22  17450  \n",
       "5       5500        19           25  15250  \n",
       "6       5500        19           25  17710  \n",
       "7       5500        19           25  18920  \n",
       "8       5500        17           20  23875  \n",
       "9       5500        16           22      ?  \n",
       "10      5800        23           29  16430  \n",
       "11      5800        23           29  16925  \n",
       "12      4250        21           28  20970  \n",
       "13      4250        21           28  21105  \n",
       "14      4250        20           25  24565  \n",
       "15      5400        16           22  30760  \n",
       "16      5400        16           22  41315  \n",
       "17      5400        15           20  36880  \n",
       "18      5100        47           53   5151  \n",
       "19      5400        38           43   6295  \n",
       "20      5400        38           43   6575  \n",
       "21      5500        37           41   5572  \n",
       "22      5500        31           38   6377  \n",
       "23      5500        24           30   7957  \n",
       "24      5500        31           38   6229  \n",
       "25      5500        31           38   6692  \n",
       "26      5500        31           38   7609  \n",
       "27      5500        24           30   8558  \n",
       "28      5000        24           30   8921  \n",
       "29      5000        19           24  12964  \n",
       "..       ...       ...          ...    ...  \n",
       "175     4200        27           32   9988  \n",
       "176     4200        27           32  10898  \n",
       "177     4200        27           32  11248  \n",
       "178     5200        20           24  16558  \n",
       "179     5200        19           24  15998  \n",
       "180     5200        20           24  15690  \n",
       "181     5200        19           24  15750  \n",
       "182     4800        37           46   7775  \n",
       "183     5250        27           34   7975  \n",
       "184     4800        37           46   7995  \n",
       "185     5250        27           34   8195  \n",
       "186     5250        27           34   8495  \n",
       "187     4500        37           42   9495  \n",
       "188     5500        26           32   9995  \n",
       "189     5500        24           29  11595  \n",
       "190     5500        24           29   9980  \n",
       "191     5500        19           24  13295  \n",
       "192     4500        33           38  13845  \n",
       "193     5500        25           31  12290  \n",
       "194     5400        23           28  12940  \n",
       "195     5400        23           28  13415  \n",
       "196     5400        24           28  15985  \n",
       "197     5400        24           28  16515  \n",
       "198     5100        17           22  18420  \n",
       "199     5100        17           22  18950  \n",
       "200     5400        23           28  16845  \n",
       "201     5300        19           25  19045  \n",
       "202     5500        18           23  21485  \n",
       "203     4800        26           27  22470  \n",
       "204     5400        19           25  22625  \n",
       "\n",
       "[205 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Select only the columns with continuous values from - https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names\n",
    "continuous_values_cols = ['normalized-losses', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'bore', 'stroke', 'compression-rate', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
    "numeric_cars = cars[continuous_values_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>?</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>?</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>?</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  normalized-losses  wheel-base  length  width  height  curb-weight  bore  \\\n",
       "0                 ?        88.6   168.8   64.1    48.8         2548  3.47   \n",
       "1                 ?        88.6   168.8   64.1    48.8         2548  3.47   \n",
       "2                 ?        94.5   171.2   65.5    52.4         2823  2.68   \n",
       "3               164        99.8   176.6   66.2    54.3         2337  3.19   \n",
       "4               164        99.4   176.6   66.4    54.3         2824  3.19   \n",
       "\n",
       "  stroke  compression-rate horsepower peak-rpm  city-mpg  highway-mpg  price  \n",
       "0   2.68               9.0        111     5000        21           27  13495  \n",
       "1   2.68               9.0        111     5000        21           27  16500  \n",
       "2   3.47               9.0        154     5000        19           26  16500  \n",
       "3   3.40              10.0        102     5500        24           30  13950  \n",
       "4   3.40               8.0        115     5500        18           22  17450  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.00</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.00</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99.8</td>\n",
       "      <td>177.3</td>\n",
       "      <td>66.3</td>\n",
       "      <td>53.1</td>\n",
       "      <td>2507</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>158</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2844</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>17710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2954</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>158</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3086</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>140</td>\n",
       "      <td>5500</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3053</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>7.00</td>\n",
       "      <td>160</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>192</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>192</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>188</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2710</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>20970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>188</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2765</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>21105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3055</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121</td>\n",
       "      <td>4250</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>24565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3230</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>30760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>193.8</td>\n",
       "      <td>67.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>3380</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>41315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>3505</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182</td>\n",
       "      <td>5400</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>36880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>121</td>\n",
       "      <td>88.4</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>53.2</td>\n",
       "      <td>1488</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.50</td>\n",
       "      <td>48</td>\n",
       "      <td>5100</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>5151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>98</td>\n",
       "      <td>94.5</td>\n",
       "      <td>155.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1874</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>81</td>\n",
       "      <td>94.5</td>\n",
       "      <td>158.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1909</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70</td>\n",
       "      <td>5400</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>6575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>118</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.41</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>118</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>118</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>2128</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>7957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>148</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1967</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>148</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>6692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>148</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>7609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>148</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>2191</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>110</td>\n",
       "      <td>103.3</td>\n",
       "      <td>174.6</td>\n",
       "      <td>64.6</td>\n",
       "      <td>59.8</td>\n",
       "      <td>2535</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>8.50</td>\n",
       "      <td>88</td>\n",
       "      <td>5000</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>145</td>\n",
       "      <td>95.9</td>\n",
       "      <td>173.2</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>2811</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.90</td>\n",
       "      <td>7.00</td>\n",
       "      <td>145</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>65</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2414</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>65</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2414</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>10898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>65</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2458</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92</td>\n",
       "      <td>4200</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>11248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>197</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2976</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>16558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>197</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3016</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>90</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3131</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>15690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3151</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156</td>\n",
       "      <td>5200</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>122</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2261</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>122</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2209</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>94</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2264</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52</td>\n",
       "      <td>4800</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>94</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2212</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>94</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2275</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85</td>\n",
       "      <td>5250</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>8495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>94</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2319</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>9495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>94</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2300</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>100</td>\n",
       "      <td>5500</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.5</td>\n",
       "      <td>159.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2254</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>256</td>\n",
       "      <td>94.5</td>\n",
       "      <td>165.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>51.4</td>\n",
       "      <td>2221</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2661</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>13295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2579</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68</td>\n",
       "      <td>4500</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>13845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>183.1</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2563</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>88</td>\n",
       "      <td>5500</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>12290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>103</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2912</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>12940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>74</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3034</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>13415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>103</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2935</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>15985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>74</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3042</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>103</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3045</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>74</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3157</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162</td>\n",
       "      <td>5100</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>18950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>95</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>95</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.70</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>95</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.80</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>95</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>95</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    normalized-losses  wheel-base  length  width  height  curb-weight  bore  \\\n",
       "0                 NaN        88.6   168.8   64.1    48.8         2548  3.47   \n",
       "1                 NaN        88.6   168.8   64.1    48.8         2548  3.47   \n",
       "2                 NaN        94.5   171.2   65.5    52.4         2823  2.68   \n",
       "3                 164        99.8   176.6   66.2    54.3         2337  3.19   \n",
       "4                 164        99.4   176.6   66.4    54.3         2824  3.19   \n",
       "5                 NaN        99.8   177.3   66.3    53.1         2507  3.19   \n",
       "6                 158       105.8   192.7   71.4    55.7         2844  3.19   \n",
       "7                 NaN       105.8   192.7   71.4    55.7         2954  3.19   \n",
       "8                 158       105.8   192.7   71.4    55.9         3086  3.13   \n",
       "9                 NaN        99.5   178.2   67.9    52.0         3053  3.13   \n",
       "10                192       101.2   176.8   64.8    54.3         2395  3.50   \n",
       "11                192       101.2   176.8   64.8    54.3         2395  3.50   \n",
       "12                188       101.2   176.8   64.8    54.3         2710  3.31   \n",
       "13                188       101.2   176.8   64.8    54.3         2765  3.31   \n",
       "14                NaN       103.5   189.0   66.9    55.7         3055  3.31   \n",
       "15                NaN       103.5   189.0   66.9    55.7         3230  3.62   \n",
       "16                NaN       103.5   193.8   67.9    53.7         3380  3.62   \n",
       "17                NaN       110.0   197.0   70.9    56.3         3505  3.62   \n",
       "18                121        88.4   141.1   60.3    53.2         1488  2.91   \n",
       "19                 98        94.5   155.9   63.6    52.0         1874  3.03   \n",
       "20                 81        94.5   158.8   63.6    52.0         1909  3.03   \n",
       "21                118        93.7   157.3   63.8    50.8         1876  2.97   \n",
       "22                118        93.7   157.3   63.8    50.8         1876  2.97   \n",
       "23                118        93.7   157.3   63.8    50.8         2128  3.03   \n",
       "24                148        93.7   157.3   63.8    50.6         1967  2.97   \n",
       "25                148        93.7   157.3   63.8    50.6         1989  2.97   \n",
       "26                148        93.7   157.3   63.8    50.6         1989  2.97   \n",
       "27                148        93.7   157.3   63.8    50.6         2191  3.03   \n",
       "28                110       103.3   174.6   64.6    59.8         2535  3.34   \n",
       "29                145        95.9   173.2   66.3    50.2         2811  3.60   \n",
       "..                ...         ...     ...    ...     ...          ...   ...   \n",
       "175                65       102.4   175.6   66.5    53.9         2414  3.31   \n",
       "176                65       102.4   175.6   66.5    54.9         2414  3.31   \n",
       "177                65       102.4   175.6   66.5    53.9         2458  3.31   \n",
       "178               197       102.9   183.5   67.7    52.0         2976  3.27   \n",
       "179               197       102.9   183.5   67.7    52.0         3016  3.27   \n",
       "180                90       104.5   187.8   66.5    54.1         3131  3.27   \n",
       "181               NaN       104.5   187.8   66.5    54.1         3151  3.27   \n",
       "182               122        97.3   171.7   65.5    55.7         2261  3.01   \n",
       "183               122        97.3   171.7   65.5    55.7         2209  3.19   \n",
       "184                94        97.3   171.7   65.5    55.7         2264  3.01   \n",
       "185                94        97.3   171.7   65.5    55.7         2212  3.19   \n",
       "186                94        97.3   171.7   65.5    55.7         2275  3.19   \n",
       "187                94        97.3   171.7   65.5    55.7         2319  3.01   \n",
       "188                94        97.3   171.7   65.5    55.7         2300  3.19   \n",
       "189               NaN        94.5   159.3   64.2    55.6         2254  3.19   \n",
       "190               256        94.5   165.7   64.0    51.4         2221  3.19   \n",
       "191               NaN       100.4   180.2   66.9    55.1         2661  3.19   \n",
       "192               NaN       100.4   180.2   66.9    55.1         2579  3.01   \n",
       "193               NaN       100.4   183.1   66.9    55.1         2563  3.19   \n",
       "194               103       104.3   188.8   67.2    56.2         2912  3.78   \n",
       "195                74       104.3   188.8   67.2    57.5         3034  3.78   \n",
       "196               103       104.3   188.8   67.2    56.2         2935  3.78   \n",
       "197                74       104.3   188.8   67.2    57.5         3042  3.78   \n",
       "198               103       104.3   188.8   67.2    56.2         3045  3.62   \n",
       "199                74       104.3   188.8   67.2    57.5         3157  3.62   \n",
       "200                95       109.1   188.8   68.9    55.5         2952  3.78   \n",
       "201                95       109.1   188.8   68.8    55.5         3049  3.78   \n",
       "202                95       109.1   188.8   68.9    55.5         3012  3.58   \n",
       "203                95       109.1   188.8   68.9    55.5         3217  3.01   \n",
       "204                95       109.1   188.8   68.9    55.5         3062  3.78   \n",
       "\n",
       "    stroke  compression-rate horsepower peak-rpm  city-mpg  highway-mpg  price  \n",
       "0     2.68              9.00        111     5000        21           27  13495  \n",
       "1     2.68              9.00        111     5000        21           27  16500  \n",
       "2     3.47              9.00        154     5000        19           26  16500  \n",
       "3     3.40             10.00        102     5500        24           30  13950  \n",
       "4     3.40              8.00        115     5500        18           22  17450  \n",
       "5     3.40              8.50        110     5500        19           25  15250  \n",
       "6     3.40              8.50        110     5500        19           25  17710  \n",
       "7     3.40              8.50        110     5500        19           25  18920  \n",
       "8     3.40              8.30        140     5500        17           20  23875  \n",
       "9     3.40              7.00        160     5500        16           22    NaN  \n",
       "10    2.80              8.80        101     5800        23           29  16430  \n",
       "11    2.80              8.80        101     5800        23           29  16925  \n",
       "12    3.19              9.00        121     4250        21           28  20970  \n",
       "13    3.19              9.00        121     4250        21           28  21105  \n",
       "14    3.19              9.00        121     4250        20           25  24565  \n",
       "15    3.39              8.00        182     5400        16           22  30760  \n",
       "16    3.39              8.00        182     5400        16           22  41315  \n",
       "17    3.39              8.00        182     5400        15           20  36880  \n",
       "18    3.03              9.50         48     5100        47           53   5151  \n",
       "19    3.11              9.60         70     5400        38           43   6295  \n",
       "20    3.11              9.60         70     5400        38           43   6575  \n",
       "21    3.23              9.41         68     5500        37           41   5572  \n",
       "22    3.23              9.40         68     5500        31           38   6377  \n",
       "23    3.39              7.60        102     5500        24           30   7957  \n",
       "24    3.23              9.40         68     5500        31           38   6229  \n",
       "25    3.23              9.40         68     5500        31           38   6692  \n",
       "26    3.23              9.40         68     5500        31           38   7609  \n",
       "27    3.39              7.60        102     5500        24           30   8558  \n",
       "28    3.46              8.50         88     5000        24           30   8921  \n",
       "29    3.90              7.00        145     5000        19           24  12964  \n",
       "..     ...               ...        ...      ...       ...          ...    ...  \n",
       "175   3.54              8.70         92     4200        27           32   9988  \n",
       "176   3.54              8.70         92     4200        27           32  10898  \n",
       "177   3.54              8.70         92     4200        27           32  11248  \n",
       "178   3.35              9.30        161     5200        20           24  16558  \n",
       "179   3.35              9.30        161     5200        19           24  15998  \n",
       "180   3.35              9.20        156     5200        20           24  15690  \n",
       "181   3.35              9.20        156     5200        19           24  15750  \n",
       "182   3.40             23.00         52     4800        37           46   7775  \n",
       "183   3.40              9.00         85     5250        27           34   7975  \n",
       "184   3.40             23.00         52     4800        37           46   7995  \n",
       "185   3.40              9.00         85     5250        27           34   8195  \n",
       "186   3.40              9.00         85     5250        27           34   8495  \n",
       "187   3.40             23.00         68     4500        37           42   9495  \n",
       "188   3.40             10.00        100     5500        26           32   9995  \n",
       "189   3.40              8.50         90     5500        24           29  11595  \n",
       "190   3.40              8.50         90     5500        24           29   9980  \n",
       "191   3.40              8.50        110     5500        19           24  13295  \n",
       "192   3.40             23.00         68     4500        33           38  13845  \n",
       "193   3.40              9.00         88     5500        25           31  12290  \n",
       "194   3.15              9.50        114     5400        23           28  12940  \n",
       "195   3.15              9.50        114     5400        23           28  13415  \n",
       "196   3.15              9.50        114     5400        24           28  15985  \n",
       "197   3.15              9.50        114     5400        24           28  16515  \n",
       "198   3.15              7.50        162     5100        17           22  18420  \n",
       "199   3.15              7.50        162     5100        17           22  18950  \n",
       "200   3.15              9.50        114     5400        23           28  16845  \n",
       "201   3.15              8.70        160     5300        19           25  19045  \n",
       "202   2.87              8.80        134     5500        18           23  21485  \n",
       "203   3.40             23.00        106     4800        26           27  22470  \n",
       "204   3.15              9.50        114     5400        19           25  22625  \n",
       "\n",
       "[205 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars = numeric_cars.replace('?', np.nan)\n",
    "numeric_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    41\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-rate      0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars = numeric_cars.astype('float')\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    37\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-rate      0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Because `price` is the column we want to predict, let's remove any rows with missing `price` values.\n",
    "numeric_cars = numeric_cars.dropna(subset=['price'])\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Replace missing values in other columns using column means.\n",
    "numeric_cars = numeric_cars.fillna(numeric_cars.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-rate     0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm that there's no more missing values!\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Normalize all columnns to range from 0 to 1 except the target column.\n",
    "price_col = numeric_cars['price']\n",
    "numeric_cars = (numeric_cars - numeric_cars.min())/(numeric_cars.max() - numeric_cars.min())\n",
    "numeric_cars['price'] = price_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "horsepower           4007.472352\n",
       "curb-weight          4437.934395\n",
       "highway-mpg          4560.327728\n",
       "width                4644.988277\n",
       "city-mpg             4772.387141\n",
       "length               5380.923534\n",
       "wheel-base           5527.700337\n",
       "compression-rate     6736.676353\n",
       "bore                 6816.853712\n",
       "height               7487.652519\n",
       "peak-rpm             7573.370090\n",
       "normalized-losses    7697.134523\n",
       "stroke               8078.491289\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def knn_train_test(train_col, target_col, df):\n",
    "    knn = KNeighborsRegressor()\n",
    "    np.random.seed(1)\n",
    "        \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    # Fit a KNN model using default k value.\n",
    "    knn.fit(train_df[[train_col]], train_df[target_col])\n",
    "    \n",
    "    # Make predictions using model.\n",
    "    predicted_labels = knn.predict(test_df[[train_col]])\n",
    "\n",
    "    # Calculate and return RMSE.\n",
    "    mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "    rmse = np.sqrt(mse)\n",
    "    return rmse\n",
    "\n",
    "rmse_results = {}\n",
    "train_cols = numeric_cars.columns.drop('price')\n",
    "\n",
    "# For each column (minus `price`), train a model, return RMSE value\n",
    "# and add to the dictionary `rmse_results`.\n",
    "for col in train_cols:\n",
    "    rmse_val = knn_train_test(col, 'price', numeric_cars)\n",
    "    rmse_results[col] = rmse_val\n",
    "\n",
    "# Create a Series object from the dictionary so \n",
    "# we can easily view the results, sort, etc\n",
    "rmse_results_series = pd.Series(rmse_results)\n",
    "rmse_results_series.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bore': {1: 7496.1492312406444,\n",
       "  3: 6936.9888741632003,\n",
       "  5: 6816.8537123691885,\n",
       "  7: 7062.0613050538341,\n",
       "  9: 6869.7274373649016},\n",
       " 'city-mpg': {1: 4540.3610032247389,\n",
       "  3: 4662.4683767438482,\n",
       "  5: 4729.6734209992692,\n",
       "  7: 5099.2742894698586,\n",
       "  9: 4999.2917237740958},\n",
       " 'compression-rate': {1: 9024.9026779536325,\n",
       "  3: 7033.5529229950389,\n",
       "  5: 6736.676353123451,\n",
       "  7: 7459.1131944220724,\n",
       "  9: 7219.385481303907},\n",
       " 'curb-weight': {1: 5518.8832374058084,\n",
       "  3: 5048.6077260366692,\n",
       "  5: 4437.9343946355393,\n",
       "  7: 4369.3490898512136,\n",
       "  9: 4632.2055452210743},\n",
       " 'height': {1: 9108.4718365936551,\n",
       "  3: 8049.9871472883196,\n",
       "  5: 7487.6525188849646,\n",
       "  7: 7753.7974180840583,\n",
       "  9: 7695.632426557866},\n",
       " 'highway-mpg': {1: 5270.360471073066,\n",
       "  3: 4618.1866223408379,\n",
       "  5: 4579.0372499290315,\n",
       "  7: 4914.2600028726101,\n",
       "  9: 5181.9124189636359},\n",
       " 'horsepower': {1: 3749.5962185254293,\n",
       "  3: 3964.9503610053594,\n",
       "  5: 4007.4723516831596,\n",
       "  7: 4391.4816735297054,\n",
       "  9: 4505.1886320053109},\n",
       " 'length': {1: 5291.7851645472883,\n",
       "  3: 5267.2167776785409,\n",
       "  5: 5382.6711551381659,\n",
       "  7: 5396.362242025737,\n",
       "  9: 5420.5479164322587},\n",
       " 'normalized-losses': {1: 7906.5941410250143,\n",
       "  3: 6712.8733553798356,\n",
       "  5: 7635.1704160923791,\n",
       "  7: 7870.6510032392407,\n",
       "  9: 8221.5784655443185},\n",
       " 'peak-rpm': {1: 9825.559283202294,\n",
       "  3: 8025.1729800507092,\n",
       "  5: 7498.7464749413657,\n",
       "  7: 7296.5172664110205,\n",
       "  9: 7239.4781688794701},\n",
       " 'stroke': {1: 7282.3488587810798,\n",
       "  3: 7664.9840308065386,\n",
       "  5: 8078.4912887356768,\n",
       "  7: 7754.4838594616886,\n",
       "  9: 7723.9131538450647},\n",
       " 'wheel-base': {1: 5964.6822353178914,\n",
       "  3: 5246.472910232148,\n",
       "  5: 5527.6824887322919,\n",
       "  7: 5485.6830335257237,\n",
       "  9: 5734.4339857054465},\n",
       " 'width': {1: 4453.161424568767,\n",
       "  3: 4697.2871145506588,\n",
       "  5: 4644.8984285434217,\n",
       "  7: 4562.1341847495605,\n",
       "  9: 4643.8823393933362}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_col, target_col, df):\n",
    "    np.random.seed(1)\n",
    "        \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    k_values = [1,3,5,7,9]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[[train_col]], train_df[target_col])\n",
    "\n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[[train_col]])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "# For each column (minus `price`), train a model, return RMSE value\n",
    "# and add to the dictionary `rmse_results`.\n",
    "train_cols = numeric_cars.columns.drop('price')\n",
    "for col in train_cols:\n",
    "    rmse_val = knn_train_test(col, 'price', numeric_cars)\n",
    "    k_rmse_results[col] = rmse_val\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGUZUDAaDwdAyjKgYDAaDoWX8f5CsFnSLoZqaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b57748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "for k,v in k_rmse_results.items():\n",
    "    x = list(v.keys())\n",
    "    y = list(v.values())\n",
    "    \n",
    "    plt.plot(x,y)\n",
    "    plt.xlabel('k value')\n",
    "    plt.ylabel('RMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "horsepower           4123.737847\n",
       "width                4600.272698\n",
       "curb-weight          4801.395999\n",
       "city-mpg             4806.213763\n",
       "highway-mpg          4912.751353\n",
       "length               5351.716651\n",
       "wheel-base           5591.790931\n",
       "bore                 7036.356112\n",
       "compression-rate     7494.726126\n",
       "normalized-losses    7669.373476\n",
       "stroke               7700.844238\n",
       "peak-rpm             7977.094835\n",
       "height               8019.108269\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute average RMSE across different `k` values for each feature.\n",
    "feature_avg_rmse = {}\n",
    "for k,v in k_rmse_results.items():\n",
    "    avg_rmse = np.mean(list(v.values()))\n",
    "    feature_avg_rmse[k] = avg_rmse\n",
    "series_avg_rmse = pd.Series(feature_avg_rmse)\n",
    "series_avg_rmse.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'five best features': {5: 3346.6737097607775},\n",
       " 'four best features': {5: 3232.1036292326721},\n",
       " 'six best features': {5: 3398.1290113563641},\n",
       " 'three best features': {5: 3212.5596306057919},\n",
       " 'two best features': {5: 3681.3980922556266}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_cols, target_col, df):\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    k_values = [5]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[train_cols], train_df[target_col])\n",
    "\n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[train_cols])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "two_best_features = ['horsepower', 'width']\n",
    "rmse_val = knn_train_test(two_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"two best features\"] = rmse_val\n",
    "\n",
    "three_best_features = ['horsepower', 'width', 'curb-weight']\n",
    "rmse_val = knn_train_test(three_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"three best features\"] = rmse_val\n",
    "\n",
    "four_best_features = ['horsepower', 'width', 'curb-weight', 'city-mpg']\n",
    "rmse_val = knn_train_test(four_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"four best features\"] = rmse_val\n",
    "\n",
    "five_best_features = ['horsepower', 'width', 'curb-weight' , 'city-mpg' , 'highway-mpg']\n",
    "rmse_val = knn_train_test(five_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"five best features\"] = rmse_val\n",
    "\n",
    "six_best_features = ['horsepower', 'width', 'curb-weight' , 'city-mpg' , 'highway-mpg', 'length']\n",
    "rmse_val = knn_train_test(six_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"six best features\"] = rmse_val\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'five best features': {1: 2561.7319037195625,\n",
       "  2: 2567.2749455482176,\n",
       "  3: 2949.9007889192553,\n",
       "  4: 3074.6091106298891,\n",
       "  5: 3346.6737097607775,\n",
       "  6: 3686.4646211770864,\n",
       "  7: 3907.1959982578019,\n",
       "  8: 4104.0339873177718,\n",
       "  9: 4335.7141974258602,\n",
       "  10: 4463.6007084810435,\n",
       "  11: 4444.0259889090448,\n",
       "  12: 4534.547516044051,\n",
       "  13: 4638.5257014541967,\n",
       "  14: 4686.7680627393893,\n",
       "  15: 4676.6172318274348,\n",
       "  16: 4706.4889916373404,\n",
       "  17: 4714.757468354599,\n",
       "  18: 4724.0179262108768,\n",
       "  19: 4780.0364569672583,\n",
       "  20: 4790.8654014852591,\n",
       "  21: 4788.4429142051176,\n",
       "  22: 4820.2560355653704,\n",
       "  23: 4823.6246116515467,\n",
       "  24: 4830.7715122893824},\n",
       " 'four best features': {1: 3135.5489073677436,\n",
       "  2: 2514.1812009849527,\n",
       "  3: 2788.5519417420178,\n",
       "  4: 2917.4679936225316,\n",
       "  5: 3232.1036292326721,\n",
       "  6: 3566.725419074407,\n",
       "  7: 3834.9804809872821,\n",
       "  8: 3927.3952487590609,\n",
       "  9: 4078.9765839753827,\n",
       "  10: 4199.8376270003955,\n",
       "  11: 4345.0069904611819,\n",
       "  12: 4451.3870113027624,\n",
       "  13: 4550.1634683008278,\n",
       "  14: 4591.5340160428832,\n",
       "  15: 4630.3996426828098,\n",
       "  16: 4711.9117982858279,\n",
       "  17: 4692.3372730081592,\n",
       "  18: 4709.1872236435829,\n",
       "  19: 4698.1962740829795,\n",
       "  20: 4738.5487814580347,\n",
       "  21: 4727.3518464816807,\n",
       "  22: 4719.3369599341022,\n",
       "  23: 4707.9563401268824,\n",
       "  24: 4753.4193738950999},\n",
       " 'three best features': {1: 3308.7499419294022,\n",
       "  2: 3044.812909435545,\n",
       "  3: 3042.2117028741623,\n",
       "  4: 2958.964739955848,\n",
       "  5: 3212.5596306057919,\n",
       "  6: 3542.3007736748041,\n",
       "  7: 3801.5597829031262,\n",
       "  8: 4007.7501484785639,\n",
       "  9: 4074.3452185932656,\n",
       "  10: 4225.0494506919176,\n",
       "  11: 4338.8991649386644,\n",
       "  12: 4428.0841388589351,\n",
       "  13: 4496.3621365502913,\n",
       "  14: 4540.1357252028592,\n",
       "  15: 4614.0272979737174,\n",
       "  16: 4654.474275823789,\n",
       "  17: 4714.0580949648638,\n",
       "  18: 4645.9886513064885,\n",
       "  19: 4628.211244787356,\n",
       "  20: 4665.0992005704829,\n",
       "  21: 4648.5009310888045,\n",
       "  22: 4610.0134050293573,\n",
       "  23: 4642.8367354686252,\n",
       "  24: 4669.5676777327653}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_cols, target_col, df):\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    k_values = [i for i in range(1, 25)]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[train_cols], train_df[target_col])\n",
    "\n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[train_cols])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "three_best_features = ['horsepower', 'width', 'curb-weight']\n",
    "rmse_val = knn_train_test(three_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"three best features\"] = rmse_val\n",
    "\n",
    "four_best_features = ['horsepower', 'width', 'curb-weight', 'city-mpg']\n",
    "rmse_val = knn_train_test(four_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"four best features\"] = rmse_val\n",
    "\n",
    "five_best_features = ['horsepower', 'width', 'curb-weight' , 'city-mpg' , 'highway-mpg']\n",
    "rmse_val = knn_train_test(five_best_features, 'price', numeric_cars)\n",
    "k_rmse_results[\"five best features\"] = rmse_val\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIAAMCIgAIy8Af9qT1VzmHomAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112837c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k,v in k_rmse_results.items():\n",
    "    x = list(v.keys())\n",
    "    y = list(v.values())\n",
    "    \n",
    "    plt.plot(x,y)\n",
    "    plt.xlabel('k value')\n",
    "    plt.ylabel('RMSE')"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
