Mission155Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction To The Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "  <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",
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       "    <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",
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       "    <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",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>stroke</th>\n",
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       "      <th>highway-mpg</th>\n",
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       "      <th>0</th>\n",
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       "      <td>88.6</td>\n",
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       "      <th>1</th>\n",
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       "      <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",
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       "      <td>111</td>\n",
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       "      <td>16500</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
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       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
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       "      <td>10.00</td>\n",
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       "      <td>5500</td>\n",
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       "      <td>30</td>\n",
       "      <td>13950</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars = numeric_cars.replace('?', np.nan)\n",
    "numeric_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cars = numeric_cars.astype('float')\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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": 9,
   "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": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm that there's no more missing values!\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "horsepower           4037.037713\n",
       "curb-weight          4401.118255\n",
       "highway-mpg          4630.026799\n",
       "width                4704.482590\n",
       "city-mpg             4766.422505\n",
       "length               5427.200961\n",
       "wheel-base           5461.553998\n",
       "compression-rate     6610.812153\n",
       "bore                 6780.627785\n",
       "normalized-losses    7330.197653\n",
       "peak-rpm             7697.459696\n",
       "stroke               8006.529545\n",
       "height               8144.441043\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'normalized-losses': {1: 7846.750605148984,\n",
       "  3: 7500.5698123109905,\n",
       "  5: 7330.197653434445,\n",
       "  7: 7756.421586234123,\n",
       "  9: 7688.096096891432},\n",
       " 'wheel-base': {1: 4493.734068810494,\n",
       "  3: 5120.161506064513,\n",
       "  5: 5461.553997873057,\n",
       "  7: 5448.1070513823315,\n",
       "  9: 5738.405685192312},\n",
       " 'length': {1: 4628.45550121557,\n",
       "  3: 5129.8358210721635,\n",
       "  5: 5427.2009608367125,\n",
       "  7: 5313.427720847974,\n",
       "  9: 5383.054514833446},\n",
       " 'width': {1: 4559.257297950061,\n",
       "  3: 4606.413692169901,\n",
       "  5: 4704.482589704386,\n",
       "  7: 4571.485046194653,\n",
       "  9: 4652.914172067787},\n",
       " 'height': {1: 8904.04645636071,\n",
       "  3: 8277.609643045525,\n",
       "  5: 8144.441042663747,\n",
       "  7: 7679.598124393773,\n",
       "  9: 7811.03606291223},\n",
       " 'curb-weight': {1: 5264.290230758878,\n",
       "  3: 5022.318011757233,\n",
       "  5: 4401.118254793124,\n",
       "  7: 4330.608104418053,\n",
       "  9: 4632.044474454401},\n",
       " 'bore': {1: 8602.58848450066,\n",
       "  3: 6984.239489480916,\n",
       "  5: 6780.627784685976,\n",
       "  7: 6878.097965921532,\n",
       "  9: 6866.808502038413},\n",
       " 'stroke': {1: 9116.495955406906,\n",
       "  3: 7338.68466990294,\n",
       "  5: 8006.529544647101,\n",
       "  7: 7803.937796804327,\n",
       "  9: 7735.554366079291},\n",
       " 'compression-rate': {1: 8087.205346523092,\n",
       "  3: 7375.063685578359,\n",
       "  5: 6610.812153159129,\n",
       "  7: 6732.801282941515,\n",
       "  9: 7024.485525463435},\n",
       " 'horsepower': {1: 4170.054848037801,\n",
       "  3: 4020.8492630885394,\n",
       "  5: 4037.0377131537603,\n",
       "  7: 4353.811860277134,\n",
       "  9: 4515.135617419103},\n",
       " 'peak-rpm': {1: 9511.480067750124,\n",
       "  3: 8537.550899973421,\n",
       "  5: 7697.4596964334805,\n",
       "  7: 7510.294160083481,\n",
       "  9: 7340.041341263401},\n",
       " 'city-mpg': {1: 5901.143574354764,\n",
       "  3: 4646.746408727155,\n",
       "  5: 4766.422505090134,\n",
       "  7: 5232.523034167316,\n",
       "  9: 5465.209492527533},\n",
       " 'highway-mpg': {1: 6025.594966720739,\n",
       "  3: 4617.305019788554,\n",
       "  5: 4630.026798588056,\n",
       "  7: 4796.061440186946,\n",
       "  9: 5278.358056953987}}"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "horsepower           4219.377860\n",
       "width                4618.910560\n",
       "curb-weight          4730.075815\n",
       "highway-mpg          5069.469256\n",
       "length               5176.394904\n",
       "city-mpg             5202.409003\n",
       "wheel-base           5252.392462\n",
       "compression-rate     7166.073599\n",
       "bore                 7222.472445\n",
       "normalized-losses    7624.407151\n",
       "stroke               8000.240467\n",
       "peak-rpm             8119.365233\n",
       "height               8163.346266\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'two best features': {5: 3589.3132622073304},\n",
       " 'three best features': {5: 3305.9401397969677},\n",
       " 'four best features': {5: 3280.3807311630976},\n",
       " 'five best features': {5: 3341.6024539726504},\n",
       " 'six best features': {5: 3628.261188214127}}"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'three best features': {1: 3013.0109985241875,\n",
       "  2: 2813.285969825997,\n",
       "  3: 3171.585284478674,\n",
       "  4: 3182.3137417981943,\n",
       "  5: 3305.9401397969677,\n",
       "  6: 3522.506848900376,\n",
       "  7: 3774.3772094554106,\n",
       "  8: 3978.969124021116,\n",
       "  9: 3992.923680588881,\n",
       "  10: 4076.2381473803043,\n",
       "  11: 4156.388331131807,\n",
       "  12: 4201.10713385948,\n",
       "  13: 4303.62676861325,\n",
       "  14: 4359.693296989702,\n",
       "  15: 4371.771103372868,\n",
       "  16: 4394.4846551644205,\n",
       "  17: 4510.399710057406,\n",
       "  18: 4584.310961865486,\n",
       "  19: 4636.62620477063,\n",
       "  20: 4664.465847866811,\n",
       "  21: 4724.096637428273,\n",
       "  22: 4752.535484102914,\n",
       "  23: 4808.703310452101,\n",
       "  24: 4858.9452710176065},\n",
       " 'four best features': {1: 2516.589279950226,\n",
       "  2: 2811.1727257604443,\n",
       "  3: 3013.3615157930335,\n",
       "  4: 3126.269482641261,\n",
       "  5: 3280.3807311630976,\n",
       "  6: 3479.5099401018138,\n",
       "  7: 3781.5612742414464,\n",
       "  8: 3988.8669577450623,\n",
       "  9: 4024.998621362245,\n",
       "  10: 4042.247226187899,\n",
       "  11: 4230.335248684068,\n",
       "  12: 4354.008675154061,\n",
       "  13: 4461.626664877788,\n",
       "  14: 4468.029845088927,\n",
       "  15: 4444.164407677133,\n",
       "  16: 4492.069868571789,\n",
       "  17: 4597.262222692034,\n",
       "  18: 4615.446316768325,\n",
       "  19: 4692.5688728042505,\n",
       "  20: 4727.815985112382,\n",
       "  21: 4738.233067652869,\n",
       "  22: 4778.176687231466,\n",
       "  23: 4811.556798910073,\n",
       "  24: 4846.548529789929},\n",
       " 'five best features': {1: 2530.0554077602005,\n",
       "  2: 2897.1757974767684,\n",
       "  3: 3183.4597426217424,\n",
       "  4: 3168.0230415758415,\n",
       "  5: 3341.6024539726504,\n",
       "  6: 3537.520542998191,\n",
       "  7: 3817.290452118825,\n",
       "  8: 4004.414529685573,\n",
       "  9: 3992.3186041830318,\n",
       "  10: 4138.728787853335,\n",
       "  11: 4342.052852829098,\n",
       "  12: 4416.145381031136,\n",
       "  13: 4493.8798141857205,\n",
       "  14: 4463.501601554571,\n",
       "  15: 4474.480783207805,\n",
       "  16: 4553.5595362768345,\n",
       "  17: 4597.001081375769,\n",
       "  18: 4655.283442648355,\n",
       "  19: 4687.900893982822,\n",
       "  20: 4712.022985543165,\n",
       "  21: 4755.856625823773,\n",
       "  22: 4788.162720030673,\n",
       "  23: 4806.923381965054,\n",
       "  24: 4879.983352195467}}"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}