LassoRidge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso and Ridge Regression\n",
    "\n",
    "### First use Linear Regression to predict automobile prices. Then apply Lasso and Ridge Regression models on the same data and compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Summary: Predict the price of a vehicle given other information about it\n",
    "* Parameters: \n",
    "* 1st argument is the location of the file (not necessarily a csv file) \n",
    "* sep specifies the separator, which can also be expressed as a regular expression. Here we trim whitespaces around the commas\n",
    "* engine represents the parsing engine. The values are c and python. The C engine is marginally faster but Python may offer more features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</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>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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</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",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...    engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...            130   \n",
       "1  convertible          rwd           front        88.6  ...            130   \n",
       "2    hatchback          rwd           front        94.5  ...            152   \n",
       "3        sedan          fwd           front        99.8  ...            109   \n",
       "4        sedan          4wd           front        99.4  ...            136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data = pd.read_csv('imports-85.data', sep = r'\\s*,\\s*', engine = 'python')\n",
    "auto_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### repale ? to nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>NaN</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</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>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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</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",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...    engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...            130   \n",
       "1  convertible          rwd           front        88.6  ...            130   \n",
       "2    hatchback          rwd           front        94.5  ...            152   \n",
       "3        sedan          fwd           front        99.8  ...            109   \n",
       "4        sedan          4wd           front        99.4  ...            136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "auto_data = auto_data.replace('?', np.nan)\n",
    "auto_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quick overview of our data. Information about numeric fields in our dataframe\n",
    "\n",
    "Note that the automobile price is not present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</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-size</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  wheel-base      length       width      height  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
       "count   205.000000   205.000000         205.000000  205.000000   205.000000  \n",
       "mean   2555.565854   126.907317          10.142537   25.219512    30.751220  \n",
       "std     520.680204    41.642693           3.972040    6.542142     6.886443  \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
       "25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
       "75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### information about all fields in datafram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>164</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>203</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>161</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "      <td>168</td>\n",
       "      <td>114</td>\n",
       "      <td>96</td>\n",
       "      <td>120</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>...</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>...</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling normalized-losses    make fuel-type aspiration  \\\n",
       "count   205.000000               164     205       205        205   \n",
       "unique         NaN                51      22         2          2   \n",
       "top            NaN               161  toyota       gas        std   \n",
       "freq           NaN                11      32       185        168   \n",
       "mean      0.834146               NaN     NaN       NaN        NaN   \n",
       "std       1.245307               NaN     NaN       NaN        NaN   \n",
       "min      -2.000000               NaN     NaN       NaN        NaN   \n",
       "25%       0.000000               NaN     NaN       NaN        NaN   \n",
       "50%       1.000000               NaN     NaN       NaN        NaN   \n",
       "75%       2.000000               NaN     NaN       NaN        NaN   \n",
       "max       3.000000               NaN     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  ...   \\\n",
       "count           203        205          205             205  205.000000  ...    \n",
       "unique            2          5            3               2         NaN  ...    \n",
       "top            four      sedan          fwd           front         NaN  ...    \n",
       "freq            114         96          120             202         NaN  ...    \n",
       "mean            NaN        NaN          NaN             NaN   98.756585  ...    \n",
       "std             NaN        NaN          NaN             NaN    6.021776  ...    \n",
       "min             NaN        NaN          NaN             NaN   86.600000  ...    \n",
       "25%             NaN        NaN          NaN             NaN   94.500000  ...    \n",
       "50%             NaN        NaN          NaN             NaN   97.000000  ...    \n",
       "75%             NaN        NaN          NaN             NaN  102.400000  ...    \n",
       "max             NaN        NaN          NaN             NaN  120.900000  ...    \n",
       "\n",
       "        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "count    205.000000          205   201     201        205.000000        203   \n",
       "unique          NaN            8    38      36               NaN         59   \n",
       "top             NaN         mpfi  3.62     3.4               NaN         68   \n",
       "freq            NaN           94    23      20               NaN         19   \n",
       "mean     126.907317          NaN   NaN     NaN         10.142537        NaN   \n",
       "std       41.642693          NaN   NaN     NaN          3.972040        NaN   \n",
       "min       61.000000          NaN   NaN     NaN          7.000000        NaN   \n",
       "25%       97.000000          NaN   NaN     NaN          8.600000        NaN   \n",
       "50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n",
       "75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \n",
       "max      326.000000          NaN   NaN     NaN         23.000000        NaN   \n",
       "\n",
       "        peak-rpm    city-mpg highway-mpg price  \n",
       "count        203  205.000000  205.000000   201  \n",
       "unique        23         NaN         NaN   186  \n",
       "top         5500         NaN         NaN  7898  \n",
       "freq          37         NaN         NaN     2  \n",
       "mean         NaN   25.219512   30.751220   NaN  \n",
       "std          NaN    6.542142    6.886443   NaN  \n",
       "min          NaN   13.000000   16.000000   NaN  \n",
       "25%          NaN   19.000000   25.000000   NaN  \n",
       "50%          NaN   24.000000   30.000000   NaN  \n",
       "75%          NaN   30.000000   34.000000   NaN  \n",
       "max          NaN   49.000000   54.000000   NaN  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning\n",
    "\n",
    "Also called data cleansing. Involves identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.\n",
    "\n",
    "* What data type is price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      201\n",
       "unique     186\n",
       "top       7898\n",
       "freq         2\n",
       "Name: price, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert the values in the price column to numeric values\n",
    "\n",
    "If conversion throws an error set to NaN (by setting errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      201.000000\n",
       "mean     13207.129353\n",
       "std       7947.066342\n",
       "min       5118.000000\n",
       "25%       7775.000000\n",
       "50%      10295.000000\n",
       "75%      16500.000000\n",
       "max      45400.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data['price'] = pd.to_numeric(auto_data['price'], errors = 'coerce')\n",
    "\n",
    "auto_data['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping a column which we deem unnecessary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</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>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</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>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling         make fuel-type aspiration num-of-doors   body-style  \\\n",
       "0          3  alfa-romero       gas        std          two  convertible   \n",
       "1          3  alfa-romero       gas        std          two  convertible   \n",
       "2          1  alfa-romero       gas        std          two    hatchback   \n",
       "3          2         audi       gas        std         four        sedan   \n",
       "4          2         audi       gas        std         four        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base  length   ...     engine-size  \\\n",
       "0          rwd           front        88.6   168.8   ...             130   \n",
       "1          rwd           front        88.6   168.8   ...             130   \n",
       "2          rwd           front        94.5   171.2   ...             152   \n",
       "3          fwd           front        99.8   176.6   ...             109   \n",
       "4          4wd           front        99.4   176.6   ...             136   \n",
       "\n",
       "   fuel-system  bore stroke compression-ratio  horsepower peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "1         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "2         mpfi  2.68   3.47               9.0         154     5000       19   \n",
       "3         mpfi  3.19    3.4              10.0         102     5500       24   \n",
       "4         mpfi  3.19    3.4               8.0         115     5500       18   \n",
       "\n",
       "  highway-mpg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data = auto_data.drop('normalized-losses', axis = 1)\n",
    "\n",
    "auto_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</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-size</th>\n",
       "      <th>compression-ratio</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>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13207.129353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7947.066342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  wheel-base      length       width      height  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \\\n",
       "count   205.000000   205.000000         205.000000  205.000000   205.000000   \n",
       "mean   2555.565854   126.907317          10.142537   25.219512    30.751220   \n",
       "std     520.680204    41.642693           3.972040    6.542142     6.886443   \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000   \n",
       "25%    2145.000000    97.000000           8.600000   19.000000    25.000000   \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000   \n",
       "75%    2935.000000   141.000000           9.400000   30.000000    34.000000   \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000   \n",
       "\n",
       "              price  \n",
       "count    201.000000  \n",
       "mean   13207.129353  \n",
       "std     7947.066342  \n",
       "min     5118.000000  \n",
       "25%     7775.000000  \n",
       "50%    10295.000000  \n",
       "75%    16500.000000  \n",
       "max    45400.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</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>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</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>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling         make fuel-type aspiration num-of-doors   body-style  \\\n",
       "0          3  alfa-romero       gas        std          two  convertible   \n",
       "1          3  alfa-romero       gas        std          two  convertible   \n",
       "2          1  alfa-romero       gas        std          two    hatchback   \n",
       "3          2         audi       gas        std         four        sedan   \n",
       "4          2         audi       gas        std         four        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base  length   ...     engine-size  \\\n",
       "0          rwd           front        88.6   168.8   ...             130   \n",
       "1          rwd           front        88.6   168.8   ...             130   \n",
       "2          rwd           front        94.5   171.2   ...             152   \n",
       "3          fwd           front        99.8   176.6   ...             109   \n",
       "4          4wd           front        99.4   176.6   ...             136   \n",
       "\n",
       "   fuel-system  bore stroke compression-ratio  horsepower peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "1         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "2         mpfi  2.68   3.47               9.0         154     5000       19   \n",
       "3         mpfi  3.19    3.4              10.0         102     5500       24   \n",
       "4         mpfi  3.19    3.4               8.0         115     5500       18   \n",
       "\n",
       "  highway-mpg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### horsepower is also non numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     203\n",
       "unique     59\n",
       "top        68\n",
       "freq       19\n",
       "Name: horsepower, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data['horsepower'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### so convert into numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "auto_data['horsepower'] = pd.to_numeric(auto_data['horsepower'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    203.000000\n",
       "mean     104.256158\n",
       "std       39.714369\n",
       "min       48.000000\n",
       "25%       70.000000\n",
       "50%       95.000000\n",
       "75%      116.000000\n",
       "max      288.000000\n",
       "Name: horsepower, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data['horsepower'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      205\n",
       "unique       7\n",
       "top       four\n",
       "freq       159\n",
       "Name: num-of-cylinders, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data['num-of-cylinders'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>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>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</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>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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling         make fuel-type aspiration num-of-doors   body-style  \\\n",
       "0          3  alfa-romero       gas        std          two  convertible   \n",
       "1          3  alfa-romero       gas        std          two  convertible   \n",
       "2          1  alfa-romero       gas        std          two    hatchback   \n",
       "3          2         audi       gas        std         four        sedan   \n",
       "4          2         audi       gas        std         four        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base  length   ...     engine-size  \\\n",
       "0          rwd           front        88.6   168.8   ...             130   \n",
       "1          rwd           front        88.6   168.8   ...             130   \n",
       "2          rwd           front        94.5   171.2   ...             152   \n",
       "3          fwd           front        99.8   176.6   ...             109   \n",
       "4          4wd           front        99.4   176.6   ...             136   \n",
       "\n",
       "   fuel-system  bore stroke  compression-ratio  horsepower peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47   2.68                9.0       111.0     5000       21   \n",
       "1         mpfi  3.47   2.68                9.0       111.0     5000       21   \n",
       "2         mpfi  2.68   3.47                9.0       154.0     5000       19   \n",
       "3         mpfi  3.19    3.4               10.0       102.0     5500       24   \n",
       "4         mpfi  3.19    3.4                8.0       115.0     5500       18   \n",
       "\n",
       "  highway-mpg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cylinders_dict = {'two': 2, \n",
    "                  'three': 3, 'four': 4, 'five': 5, 'six': 6, 'eight': 8, 'twelve': 12}\n",
    "auto_data['num-of-cylinders'].replace(cylinders_dict, inplace=True)\n",
    "\n",
    "auto_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All other non-numeric fields can be made into usable features by applying one-hot-encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "auto_data = pd.get_dummies(auto_data, columns = [\n",
    "                                    'make', \n",
    "                                    'fuel-type', \n",
    "                                    'aspiration', \n",
    "                                    'num-of-doors', \n",
    "                                    'body-style', \n",
    "                                    'drive-wheels', \n",
    "                                    'engine-location', \n",
    "                                    'engine-type', \n",
    "                                    'fuel-system'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-type_ohcv</th>\n",
       "      <th>engine-type_rotor</th>\n",
       "      <th>fuel-system_1bbl</th>\n",
       "      <th>fuel-system_2bbl</th>\n",
       "      <th>fuel-system_4bbl</th>\n",
       "      <th>fuel-system_idi</th>\n",
       "      <th>fuel-system_mfi</th>\n",
       "      <th>fuel-system_mpfi</th>\n",
       "      <th>fuel-system_spdi</th>\n",
       "      <th>fuel-system_spfi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>6</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  wheel-base  length  width  height  curb-weight  \\\n",
       "0          3        88.6   168.8   64.1    48.8         2548   \n",
       "1          3        88.6   168.8   64.1    48.8         2548   \n",
       "2          1        94.5   171.2   65.5    52.4         2823   \n",
       "3          2        99.8   176.6   66.2    54.3         2337   \n",
       "4          2        99.4   176.6   66.4    54.3         2824   \n",
       "\n",
       "   num-of-cylinders  engine-size  bore stroke        ...         \\\n",
       "0                 4          130  3.47   2.68        ...          \n",
       "1                 4          130  3.47   2.68        ...          \n",
       "2                 6          152  2.68   3.47        ...          \n",
       "3                 4          109  3.19    3.4        ...          \n",
       "4                 5          136  3.19    3.4        ...          \n",
       "\n",
       "   engine-type_ohcv  engine-type_rotor fuel-system_1bbl  fuel-system_2bbl  \\\n",
       "0                 0                  0                0                 0   \n",
       "1                 0                  0                0                 0   \n",
       "2                 1                  0                0                 0   \n",
       "3                 0                  0                0                 0   \n",
       "4                 0                  0                0                 0   \n",
       "\n",
       "   fuel-system_4bbl  fuel-system_idi  fuel-system_mfi  fuel-system_mpfi  \\\n",
       "0                 0                0                0                 1   \n",
       "1                 0                0                0                 1   \n",
       "2                 0                0                0                 1   \n",
       "3                 0                0                0                 1   \n",
       "4                 0                0                0                 1   \n",
       "\n",
       "   fuel-system_spdi  fuel-system_spfi  \n",
       "0                 0                 0  \n",
       "1                 0                 0  \n",
       "2                 0                 0  \n",
       "3                 0                 0  \n",
       "4                 0                 0  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop rows containing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-type_ohcv</th>\n",
       "      <th>engine-type_rotor</th>\n",
       "      <th>fuel-system_1bbl</th>\n",
       "      <th>fuel-system_2bbl</th>\n",
       "      <th>fuel-system_4bbl</th>\n",
       "      <th>fuel-system_idi</th>\n",
       "      <th>fuel-system_mfi</th>\n",
       "      <th>fuel-system_mpfi</th>\n",
       "      <th>fuel-system_spdi</th>\n",
       "      <th>fuel-system_spfi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>6</td>\n",
       "      <td>152</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</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>5</td>\n",
       "      <td>131</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>108</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</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>4</td>\n",
       "      <td>108</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</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>6</td>\n",
       "      <td>164</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</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>6</td>\n",
       "      <td>164</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</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>6</td>\n",
       "      <td>164</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</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>6</td>\n",
       "      <td>209</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</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>6</td>\n",
       "      <td>209</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</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>6</td>\n",
       "      <td>209</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</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>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>90</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</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>4</td>\n",
       "      <td>98</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>122</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>156</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>86.6</td>\n",
       "      <td>144.6</td>\n",
       "      <td>63.9</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1713</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>122</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>122</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>122</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>3</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>6</td>\n",
       "      <td>171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</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>6</td>\n",
       "      <td>171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-1</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>6</td>\n",
       "      <td>171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-1</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>6</td>\n",
       "      <td>161</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>3</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0</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>5</td>\n",
       "      <td>136</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</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>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</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>4</td>\n",
       "      <td>109</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-2</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-2</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-2</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>130</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</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>6</td>\n",
       "      <td>173</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</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>6</td>\n",
       "      <td>145</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</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>4</td>\n",
       "      <td>141</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  wheel-base  length  width  height  curb-weight  \\\n",
       "0            3        88.6   168.8   64.1    48.8         2548   \n",
       "1            3        88.6   168.8   64.1    48.8         2548   \n",
       "2            1        94.5   171.2   65.5    52.4         2823   \n",
       "3            2        99.8   176.6   66.2    54.3         2337   \n",
       "4            2        99.4   176.6   66.4    54.3         2824   \n",
       "5            2        99.8   177.3   66.3    53.1         2507   \n",
       "6            1       105.8   192.7   71.4    55.7         2844   \n",
       "7            1       105.8   192.7   71.4    55.7         2954   \n",
       "8            1       105.8   192.7   71.4    55.9         3086   \n",
       "10           2       101.2   176.8   64.8    54.3         2395   \n",
       "11           0       101.2   176.8   64.8    54.3         2395   \n",
       "12           0       101.2   176.8   64.8    54.3         2710   \n",
       "13           0       101.2   176.8   64.8    54.3         2765   \n",
       "14           1       103.5   189.0   66.9    55.7         3055   \n",
       "15           0       103.5   189.0   66.9    55.7         3230   \n",
       "16           0       103.5   193.8   67.9    53.7         3380   \n",
       "17           0       110.0   197.0   70.9    56.3         3505   \n",
       "18           2        88.4   141.1   60.3    53.2         1488   \n",
       "19           1        94.5   155.9   63.6    52.0         1874   \n",
       "20           0        94.5   158.8   63.6    52.0         1909   \n",
       "21           1        93.7   157.3   63.8    50.8         1876   \n",
       "22           1        93.7   157.3   63.8    50.8         1876   \n",
       "23           1        93.7   157.3   63.8    50.8         2128   \n",
       "24           1        93.7   157.3   63.8    50.6         1967   \n",
       "25           1        93.7   157.3   63.8    50.6         1989   \n",
       "26           1        93.7   157.3   63.8    50.6         1989   \n",
       "27           1        93.7   157.3   63.8    50.6         2191   \n",
       "28          -1       103.3   174.6   64.6    59.8         2535   \n",
       "29           3        95.9   173.2   66.3    50.2         2811   \n",
       "30           2        86.6   144.6   63.9    50.8         1713   \n",
       "..         ...         ...     ...    ...     ...          ...   \n",
       "175         -1       102.4   175.6   66.5    53.9         2414   \n",
       "176         -1       102.4   175.6   66.5    54.9         2414   \n",
       "177         -1       102.4   175.6   66.5    53.9         2458   \n",
       "178          3       102.9   183.5   67.7    52.0         2976   \n",
       "179          3       102.9   183.5   67.7    52.0         3016   \n",
       "180         -1       104.5   187.8   66.5    54.1         3131   \n",
       "181         -1       104.5   187.8   66.5    54.1         3151   \n",
       "182          2        97.3   171.7   65.5    55.7         2261   \n",
       "183          2        97.3   171.7   65.5    55.7         2209   \n",
       "184          2        97.3   171.7   65.5    55.7         2264   \n",
       "185          2        97.3   171.7   65.5    55.7         2212   \n",
       "186          2        97.3   171.7   65.5    55.7         2275   \n",
       "187          2        97.3   171.7   65.5    55.7         2319   \n",
       "188          2        97.3   171.7   65.5    55.7         2300   \n",
       "189          3        94.5   159.3   64.2    55.6         2254   \n",
       "190          3        94.5   165.7   64.0    51.4         2221   \n",
       "191          0       100.4   180.2   66.9    55.1         2661   \n",
       "192          0       100.4   180.2   66.9    55.1         2579   \n",
       "193          0       100.4   183.1   66.9    55.1         2563   \n",
       "194         -2       104.3   188.8   67.2    56.2         2912   \n",
       "195         -1       104.3   188.8   67.2    57.5         3034   \n",
       "196         -2       104.3   188.8   67.2    56.2         2935   \n",
       "197         -1       104.3   188.8   67.2    57.5         3042   \n",
       "198         -2       104.3   188.8   67.2    56.2         3045   \n",
       "199         -1       104.3   188.8   67.2    57.5         3157   \n",
       "200         -1       109.1   188.8   68.9    55.5         2952   \n",
       "201         -1       109.1   188.8   68.8    55.5         3049   \n",
       "202         -1       109.1   188.8   68.9    55.5         3012   \n",
       "203         -1       109.1   188.8   68.9    55.5         3217   \n",
       "204         -1       109.1   188.8   68.9    55.5         3062   \n",
       "\n",
       "     num-of-cylinders  engine-size  bore stroke        ...         \\\n",
       "0                   4          130  3.47   2.68        ...          \n",
       "1                   4          130  3.47   2.68        ...          \n",
       "2                   6          152  2.68   3.47        ...          \n",
       "3                   4          109  3.19    3.4        ...          \n",
       "4                   5          136  3.19    3.4        ...          \n",
       "5                   5          136  3.19    3.4        ...          \n",
       "6                   5          136  3.19    3.4        ...          \n",
       "7                   5          136  3.19    3.4        ...          \n",
       "8                   5          131  3.13    3.4        ...          \n",
       "10                  4          108   3.5    2.8        ...          \n",
       "11                  4          108   3.5    2.8        ...          \n",
       "12                  6          164  3.31   3.19        ...          \n",
       "13                  6          164  3.31   3.19        ...          \n",
       "14                  6          164  3.31   3.19        ...          \n",
       "15                  6          209  3.62   3.39        ...          \n",
       "16                  6          209  3.62   3.39        ...          \n",
       "17                  6          209  3.62   3.39        ...          \n",
       "18                  3           61  2.91   3.03        ...          \n",
       "19                  4           90  3.03   3.11        ...          \n",
       "20                  4           90  3.03   3.11        ...          \n",
       "21                  4           90  2.97   3.23        ...          \n",
       "22                  4           90  2.97   3.23        ...          \n",
       "23                  4           98  3.03   3.39        ...          \n",
       "24                  4           90  2.97   3.23        ...          \n",
       "25                  4           90  2.97   3.23        ...          \n",
       "26                  4           90  2.97   3.23        ...          \n",
       "27                  4           98  3.03   3.39        ...          \n",
       "28                  4          122  3.34   3.46        ...          \n",
       "29                  4          156   3.6    3.9        ...          \n",
       "30                  4           92  2.91   3.41        ...          \n",
       "..                ...          ...   ...    ...        ...          \n",
       "175                 4          122  3.31   3.54        ...          \n",
       "176                 4          122  3.31   3.54        ...          \n",
       "177                 4          122  3.31   3.54        ...          \n",
       "178                 6          171  3.27   3.35        ...          \n",
       "179                 6          171  3.27   3.35        ...          \n",
       "180                 6          171  3.27   3.35        ...          \n",
       "181                 6          161  3.27   3.35        ...          \n",
       "182                 4           97  3.01    3.4        ...          \n",
       "183                 4          109  3.19    3.4        ...          \n",
       "184                 4           97  3.01    3.4        ...          \n",
       "185                 4          109  3.19    3.4        ...          \n",
       "186                 4          109  3.19    3.4        ...          \n",
       "187                 4           97  3.01    3.4        ...          \n",
       "188                 4          109  3.19    3.4        ...          \n",
       "189                 4          109  3.19    3.4        ...          \n",
       "190                 4          109  3.19    3.4        ...          \n",
       "191                 5          136  3.19    3.4        ...          \n",
       "192                 4           97  3.01    3.4        ...          \n",
       "193                 4          109  3.19    3.4        ...          \n",
       "194                 4          141  3.78   3.15        ...          \n",
       "195                 4          141  3.78   3.15        ...          \n",
       "196                 4          141  3.78   3.15        ...          \n",
       "197                 4          141  3.78   3.15        ...          \n",
       "198                 4          130  3.62   3.15        ...          \n",
       "199                 4          130  3.62   3.15        ...          \n",
       "200                 4          141  3.78   3.15        ...          \n",
       "201                 4          141  3.78   3.15        ...          \n",
       "202                 6          173  3.58   2.87        ...          \n",
       "203                 6          145  3.01    3.4        ...          \n",
       "204                 4          141  3.78   3.15        ...          \n",
       "\n",
       "     engine-type_ohcv  engine-type_rotor fuel-system_1bbl  fuel-system_2bbl  \\\n",
       "0                   0                  0                0                 0   \n",
       "1                   0                  0                0                 0   \n",
       "2                   1                  0                0                 0   \n",
       "3                   0                  0                0                 0   \n",
       "4                   0                  0                0                 0   \n",
       "5                   0                  0                0                 0   \n",
       "6                   0                  0                0                 0   \n",
       "7                   0                  0                0                 0   \n",
       "8                   0                  0                0                 0   \n",
       "10                  0                  0                0                 0   \n",
       "11                  0                  0                0                 0   \n",
       "12                  0                  0                0                 0   \n",
       "13                  0                  0                0                 0   \n",
       "14                  0                  0                0                 0   \n",
       "15                  0                  0                0                 0   \n",
       "16                  0                  0                0                 0   \n",
       "17                  0                  0                0                 0   \n",
       "18                  0                  0                0                 1   \n",
       "19                  0                  0                0                 1   \n",
       "20                  0                  0                0                 1   \n",
       "21                  0                  0                0                 1   \n",
       "22                  0                  0                0                 1   \n",
       "23                  0                  0                0                 0   \n",
       "24                  0                  0                0                 1   \n",
       "25                  0                  0                0                 1   \n",
       "26                  0                  0                0                 1   \n",
       "27                  0                  0                0                 0   \n",
       "28                  0                  0                0                 1   \n",
       "29                  0                  0                0                 0   \n",
       "30                  0                  0                1                 0   \n",
       "..                ...                ...              ...               ...   \n",
       "175                 0                  0                0                 0   \n",
       "176                 0                  0                0                 0   \n",
       "177                 0                  0                0                 0   \n",
       "178                 0                  0                0                 0   \n",
       "179                 0                  0                0                 0   \n",
       "180                 0                  0                0                 0   \n",
       "181                 0                  0                0                 0   \n",
       "182                 0                  0                0                 0   \n",
       "183                 0                  0                0                 0   \n",
       "184                 0                  0                0                 0   \n",
       "185                 0                  0                0                 0   \n",
       "186                 0                  0                0                 0   \n",
       "187                 0                  0                0                 0   \n",
       "188                 0                  0                0                 0   \n",
       "189                 0                  0                0                 0   \n",
       "190                 0                  0                0                 0   \n",
       "191                 0                  0                0                 0   \n",
       "192                 0                  0                0                 0   \n",
       "193                 0                  0                0                 0   \n",
       "194                 0                  0                0                 0   \n",
       "195                 0                  0                0                 0   \n",
       "196                 0                  0                0                 0   \n",
       "197                 0                  0                0                 0   \n",
       "198                 0                  0                0                 0   \n",
       "199                 0                  0                0                 0   \n",
       "200                 0                  0                0                 0   \n",
       "201                 0                  0                0                 0   \n",
       "202                 1                  0                0                 0   \n",
       "203                 0                  0                0                 0   \n",
       "204                 0                  0                0                 0   \n",
       "\n",
       "     fuel-system_4bbl  fuel-system_idi  fuel-system_mfi  fuel-system_mpfi  \\\n",
       "0                   0                0                0                 1   \n",
       "1                   0                0                0                 1   \n",
       "2                   0                0                0                 1   \n",
       "3                   0                0                0                 1   \n",
       "4                   0                0                0                 1   \n",
       "5                   0                0                0                 1   \n",
       "6                   0                0                0                 1   \n",
       "7                   0                0                0                 1   \n",
       "8                   0                0                0                 1   \n",
       "10                  0                0                0                 1   \n",
       "11                  0                0                0                 1   \n",
       "12                  0                0                0                 1   \n",
       "13                  0                0                0                 1   \n",
       "14                  0                0                0                 1   \n",
       "15                  0                0                0                 1   \n",
       "16                  0                0                0                 1   \n",
       "17                  0                0                0                 1   \n",
       "18                  0                0                0                 0   \n",
       "19                  0                0                0                 0   \n",
       "20                  0                0                0                 0   \n",
       "21                  0                0                0                 0   \n",
       "22                  0                0                0                 0   \n",
       "23                  0                0                0                 1   \n",
       "24                  0                0                0                 0   \n",
       "25                  0                0                0                 0   \n",
       "26                  0                0                0                 0   \n",
       "27                  0                0                0                 1   \n",
       "28                  0                0                0                 0   \n",
       "29                  0                0                1                 0   \n",
       "30                  0                0                0                 0   \n",
       "..                ...              ...              ...               ...   \n",
       "175                 0                0                0                 1   \n",
       "176                 0                0                0                 1   \n",
       "177                 0                0                0                 1   \n",
       "178                 0                0                0                 1   \n",
       "179                 0                0                0                 1   \n",
       "180                 0                0                0                 1   \n",
       "181                 0                0                0                 1   \n",
       "182                 0                1                0                 0   \n",
       "183                 0                0                0                 1   \n",
       "184                 0                1                0                 0   \n",
       "185                 0                0                0                 1   \n",
       "186                 0                0                0                 1   \n",
       "187                 0                1                0                 0   \n",
       "188                 0                0                0                 1   \n",
       "189                 0                0                0                 1   \n",
       "190                 0                0                0                 1   \n",
       "191                 0                0                0                 1   \n",
       "192                 0                1                0                 0   \n",
       "193                 0                0                0                 1   \n",
       "194                 0                0                0                 1   \n",
       "195                 0                0                0                 1   \n",
       "196                 0                0                0                 1   \n",
       "197                 0                0                0                 1   \n",
       "198                 0                0                0                 1   \n",
       "199                 0                0                0                 1   \n",
       "200                 0                0                0                 1   \n",
       "201                 0                0                0                 1   \n",
       "202                 0                0                0                 1   \n",
       "203                 0                1                0                 0   \n",
       "204                 0                0                0                 1   \n",
       "\n",
       "     fuel-system_spdi  fuel-system_spfi  \n",
       "0                   0                 0  \n",
       "1                   0                 0  \n",
       "2                   0                 0  \n",
       "3                   0                 0  \n",
       "4                   0                 0  \n",
       "5                   0                 0  \n",
       "6                   0                 0  \n",
       "7                   0                 0  \n",
       "8                   0                 0  \n",
       "10                  0                 0  \n",
       "11                  0                 0  \n",
       "12                  0                 0  \n",
       "13                  0                 0  \n",
       "14                  0                 0  \n",
       "15                  0                 0  \n",
       "16                  0                 0  \n",
       "17                  0                 0  \n",
       "18                  0                 0  \n",
       "19                  0                 0  \n",
       "20                  0                 0  \n",
       "21                  0                 0  \n",
       "22                  0                 0  \n",
       "23                  0                 0  \n",
       "24                  0                 0  \n",
       "25                  0                 0  \n",
       "26                  0                 0  \n",
       "27                  0                 0  \n",
       "28                  0                 0  \n",
       "29                  0                 0  \n",
       "30                  0                 0  \n",
       "..                ...               ...  \n",
       "175                 0                 0  \n",
       "176                 0                 0  \n",
       "177                 0                 0  \n",
       "178                 0                 0  \n",
       "179                 0                 0  \n",
       "180                 0                 0  \n",
       "181                 0                 0  \n",
       "182                 0                 0  \n",
       "183                 0                 0  \n",
       "184                 0                 0  \n",
       "185                 0                 0  \n",
       "186                 0                 0  \n",
       "187                 0                 0  \n",
       "188                 0                 0  \n",
       "189                 0                 0  \n",
       "190                 0                 0  \n",
       "191                 0                 0  \n",
       "192                 0                 0  \n",
       "193                 0                 0  \n",
       "194                 0                 0  \n",
       "195                 0                 0  \n",
       "196                 0                 0  \n",
       "197                 0                 0  \n",
       "198                 0                 0  \n",
       "199                 0                 0  \n",
       "200                 0                 0  \n",
       "201                 0                 0  \n",
       "202                 0                 0  \n",
       "203                 0                 0  \n",
       "204                 0                 0  \n",
       "\n",
       "[195 rows x 69 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data = auto_data.dropna()\n",
    "auto_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify that there are no null values in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-type_ohcv</th>\n",
       "      <th>engine-type_rotor</th>\n",
       "      <th>fuel-system_1bbl</th>\n",
       "      <th>fuel-system_2bbl</th>\n",
       "      <th>fuel-system_4bbl</th>\n",
       "      <th>fuel-system_idi</th>\n",
       "      <th>fuel-system_mfi</th>\n",
       "      <th>fuel-system_mpfi</th>\n",
       "      <th>fuel-system_spdi</th>\n",
       "      <th>fuel-system_spfi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [symboling, wheel-base, length, width, height, curb-weight, num-of-cylinders, engine-size, bore, stroke, compression-ratio, horsepower, peak-rpm, city-mpg, highway-mpg, price, make_alfa-romero, make_audi, make_bmw, make_chevrolet, make_dodge, make_honda, make_isuzu, make_jaguar, make_mazda, make_mercedes-benz, make_mercury, make_mitsubishi, make_nissan, make_peugot, make_plymouth, make_porsche, make_renault, make_saab, make_subaru, make_toyota, make_volkswagen, make_volvo, fuel-type_diesel, fuel-type_gas, aspiration_std, aspiration_turbo, num-of-doors_four, num-of-doors_two, body-style_convertible, body-style_hardtop, body-style_hatchback, body-style_sedan, body-style_wagon, drive-wheels_4wd, drive-wheels_fwd, drive-wheels_rwd, engine-location_front, engine-location_rear, engine-type_dohc, engine-type_dohcv, engine-type_l, engine-type_ohc, engine-type_ohcf, engine-type_ohcv, engine-type_rotor, fuel-system_1bbl, fuel-system_2bbl, fuel-system_4bbl, fuel-system_idi, fuel-system_mfi, fuel-system_mpfi, fuel-system_spdi, fuel-system_spfi]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 69 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_data[auto_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning is now complete\n",
    "We can now use our data to build our models\n",
    "\n",
    "### Create training and test data using train_test_split¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = auto_data.drop('price', axis = 1)\n",
    "\n",
    "# Taking the labels (price)\n",
    "y = auto_data['price']\n",
    "\n",
    "# Spliting into 80% for training set and 20% for testing set so we can see our accuracy\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a LinearRegression model with our training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96792273709243304"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### view coeficients of each score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.80061464e+02,   3.06874682e+02,  -1.06499194e+02,\n",
       "         1.52236727e+02,  -5.05771408e+02,   5.30902624e+00,\n",
       "        -4.15988710e+03,   2.82885047e+02,  -1.75044342e+04,\n",
       "        -4.49860407e+03,  -1.59555122e+03,  -4.82296063e+01,\n",
       "         2.15673623e+00,  -6.79852969e+01,   1.73362582e+02,\n",
       "        -3.74284422e+03,   3.71721668e+03,   6.86565908e+03,\n",
       "        -4.12474818e+03,  -4.48093478e+03,  -2.94555422e+02,\n",
       "        -4.74283895e+03,   3.47319261e+03,  -4.08732786e+02,\n",
       "         3.02841192e+03,   2.30284405e+03,  -4.71448781e+03,\n",
       "        -6.67651176e+02,  -8.40276958e+03,  -5.25933085e+03,\n",
       "         1.04713265e+04,  -1.81898940e-11,   8.36042927e+03,\n",
       "        -4.81543507e+02,  -2.06062392e+03,   6.21153583e+01,\n",
       "         1.09986569e+03,   6.06615966e+03,  -6.06615966e+03,\n",
       "        -1.38232301e+03,   1.38232301e+03,   3.37569880e+03,\n",
       "         3.28229109e+03,   2.99875643e+03,  -1.00643481e+03,\n",
       "        -1.20820627e+03,  -3.92414032e+02,  -3.91701310e+02,\n",
       "        -4.99487338e+02,  -6.59140532e+02,   1.15862787e+03,\n",
       "        -2.46748485e+03,   2.46748485e+03,  -2.04927851e+02,\n",
       "         0.00000000e+00,   2.88374518e+03,  -9.80899556e+02,\n",
       "         1.98594135e+03,  -3.68385912e+03,   0.00000000e+00,\n",
       "        -3.85312742e+03,  -5.52868364e+02,   0.00000000e+00,\n",
       "         6.06615966e+03,  -1.73448716e+03,  -7.37469779e+02,\n",
       "        -2.06266033e+03,   2.87445339e+03])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A better view of the coefficients\n",
    "\n",
    "List of features and their coefficients, ordered by coefficient value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bore                     -1.750443e+04\n",
      "make_peugot              -8.402770e+03\n",
      "fuel-type_gas            -6.066160e+03\n",
      "make_plymouth            -5.259331e+03\n",
      "make_isuzu               -4.742839e+03\n",
      "make_mitsubishi          -4.714488e+03\n",
      "stroke                   -4.498604e+03\n",
      "make_dodge               -4.480935e+03\n",
      "num-of-cylinders         -4.159887e+03\n",
      "make_chevrolet           -4.124748e+03\n",
      "fuel-system_1bbl         -3.853127e+03\n",
      "make_alfa-romero         -3.742844e+03\n",
      "engine-type_ohcv         -3.683859e+03\n",
      "engine-location_front    -2.467485e+03\n",
      "fuel-system_spdi         -2.062660e+03\n",
      "make_toyota              -2.060624e+03\n",
      "fuel-system_mfi          -1.734487e+03\n",
      "compression-ratio        -1.595551e+03\n",
      "aspiration_std           -1.382323e+03\n",
      "body-style_hatchback     -1.208206e+03\n",
      "body-style_hardtop       -1.006435e+03\n",
      "engine-type_ohc          -9.808996e+02\n",
      "fuel-system_mpfi         -7.374698e+02\n",
      "make_nissan              -6.676512e+02\n",
      "drive-wheels_fwd         -6.591405e+02\n",
      "symboling                -5.800615e+02\n",
      "fuel-system_2bbl         -5.528684e+02\n",
      "height                   -5.057714e+02\n",
      "drive-wheels_4wd         -4.994873e+02\n",
      "make_subaru              -4.815435e+02\n",
      "                              ...     \n",
      "make_renault             -1.818989e-11\n",
      "engine-type_rotor         0.000000e+00\n",
      "engine-type_dohcv         0.000000e+00\n",
      "fuel-system_4bbl          0.000000e+00\n",
      "peak-rpm                  2.156736e+00\n",
      "curb-weight               5.309026e+00\n",
      "make_volkswagen           6.211536e+01\n",
      "width                     1.522367e+02\n",
      "highway-mpg               1.733626e+02\n",
      "engine-size               2.828850e+02\n",
      "wheel-base                3.068747e+02\n",
      "make_volvo                1.099866e+03\n",
      "drive-wheels_rwd          1.158628e+03\n",
      "aspiration_turbo          1.382323e+03\n",
      "engine-type_ohcf          1.985941e+03\n",
      "make_mercury              2.302844e+03\n",
      "engine-location_rear      2.467485e+03\n",
      "fuel-system_spfi          2.874453e+03\n",
      "engine-type_l             2.883745e+03\n",
      "body-style_convertible    2.998756e+03\n",
      "make_mercedes-benz        3.028412e+03\n",
      "num-of-doors_two          3.282291e+03\n",
      "num-of-doors_four         3.375699e+03\n",
      "make_jaguar               3.473193e+03\n",
      "make_audi                 3.717217e+03\n",
      "fuel-system_idi           6.066160e+03\n",
      "fuel-type_diesel          6.066160e+03\n",
      "make_bmw                  6.865659e+03\n",
      "make_saab                 8.360429e+03\n",
      "make_porsche              1.047133e+04\n",
      "Length: 68, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictors = x_train.columns\n",
    "\n",
    "coef = pd.Series(linear_model.coef_, predictors).sort_values()\n",
    "\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = linear_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare predicted and actual values of Price¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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JiMpQRfpls1BuZBMBpiOjnWlLFcqNABEgGvThMVD1griSgkURERl7pmlyJbm7E6pjKmTN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\nEEIIIUQr/w/LWOKw+NZHZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21880fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (15,6)\n",
    "\n",
    "plt.plot(y_predict, label = 'predicted')\n",
    "plt.plot(y_test.values, label = 'Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R-square score\n",
    "For our model, how well do the features describe the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63225834161155436"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_squere = linear_model.score(x_test, y_test)\n",
    "r_squere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Mean Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26099937.604351308"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linear_model_mse = mean_squared_error(y_predict, y_test)\n",
    "linear_model_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root of Mean Square Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5108.8098031098507"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "rmse = sqrt(linear_model_mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression\n",
    "* Cost Function: RSS + α*(sum of absolute values of coefficients)\n",
    "* RSS = Residual Sum of Squares\n",
    "* Larger values of α should result in smaller coefficients as the cost function needs to be minimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=True, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso_model = Lasso(alpha = 0.5, normalize = True)\n",
    "lasso_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check R-square on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96510812725275497"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bore                     -9.092176e+03\n",
      "make_peugot              -6.467778e+03\n",
      "make_jaguar              -6.193545e+03\n",
      "engine-location_front    -4.537584e+03\n",
      "engine-type_ohcv         -4.223274e+03\n",
      "make_plymouth            -3.546019e+03\n",
      "stroke                   -3.543804e+03\n",
      "make_dodge               -3.152300e+03\n",
      "make_mitsubishi          -3.142282e+03\n",
      "make_chevrolet           -2.942939e+03\n",
      "make_isuzu               -2.910845e+03\n",
      "aspiration_std           -2.498593e+03\n",
      "fuel-system_1bbl         -2.184594e+03\n",
      "num-of-cylinders         -1.361323e+03\n",
      "make_toyota              -1.303444e+03\n",
      "engine-type_dohc         -1.156469e+03\n",
      "drive-wheels_fwd         -9.650933e+02\n",
      "symboling                -5.782481e+02\n",
      "make_nissan              -5.647727e+02\n",
      "compression-ratio        -3.531524e+02\n",
      "height                   -3.477801e+02\n",
      "body-style_hatchback     -3.119457e+02\n",
      "make_alfa-romero         -2.269308e+02\n",
      "body-style_hardtop       -1.024258e+02\n",
      "length                   -8.684394e+01\n",
      "fuel-system_spdi         -7.328266e+01\n",
      "fuel-system_mpfi         -4.383129e+01\n",
      "drive-wheels_4wd          0.000000e+00\n",
      "engine-type_ohc          -0.000000e+00\n",
      "engine-type_dohcv         0.000000e+00\n",
      "                              ...     \n",
      "city-mpg                 -0.000000e+00\n",
      "make_mercury              0.000000e+00\n",
      "aspiration_turbo          9.267945e-11\n",
      "peak-rpm                  1.970555e+00\n",
      "curb-weight               3.968390e+00\n",
      "num-of-doors_two          1.270225e+01\n",
      "fuel-system_mfi           6.146399e+01\n",
      "body-style_sedan          9.572899e+01\n",
      "highway-mpg               1.068108e+02\n",
      "make_volvo                1.445752e+02\n",
      "width                     1.874224e+02\n",
      "engine-size               1.895810e+02\n",
      "wheel-base                2.402207e+02\n",
      "engine-type_ohcf          2.660292e+02\n",
      "num-of-doors_four         2.970371e+02\n",
      "drive-wheels_rwd          3.464650e+02\n",
      "make_honda                4.344793e+02\n",
      "make_mazda                4.668807e+02\n",
      "fuel-system_2bbl          6.156961e+02\n",
      "fuel-system_idi           7.518829e+02\n",
      "make_volkswagen           1.190833e+03\n",
      "fuel-system_spfi          2.703127e+03\n",
      "fuel-type_diesel          2.875479e+03\n",
      "body-style_convertible    3.621342e+03\n",
      "make_audi                 3.866295e+03\n",
      "engine-type_l             4.268718e+03\n",
      "make_mercedes-benz        4.728695e+03\n",
      "make_bmw                  6.586646e+03\n",
      "make_saab                 6.762817e+03\n",
      "make_porsche              7.787668e+03\n",
      "Length: 68, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(lasso_model.coef_, predictors).sort_values()\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check R-square on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = lasso_model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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B7ASd4RZS+QLW2vouTuQS1c4sfhh4B1Bx7/cASWttyb0/Aux0v98JnAFwH0+5588dv+Sa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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21b91b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (15,6)\n",
    "\n",
    "plt.plot(y_predict, label = 'predicted')\n",
    "plt.plot(y_test.values, label = 'Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check R-square value on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.887194953445\n"
     ]
    }
   ],
   "source": [
    "r_square = lasso_model.score(x_test, y_test)\n",
    "print(r_square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is the root mean square error any better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2829.518800848218"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(lasso_model_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Ridge Regression\n",
    "* Cost Function: RSS + α*(sum of squares of coefficients)\n",
    "* RSS = Residual Sum of Squares\n",
    "* Larger values of α should result in smaller coefficients as the cost function needs to be minimized\n",
    "* Ridge Regression penalizes large coefficients even more than Lasso as coefficients are squared in cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=True, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_model = Ridge(alpha = 0.5, normalize = True)\n",
    "ridge_model.fit(x_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check R-square on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92693060788890935"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engine-location_front    -3919.992407\n",
      "make_isuzu               -1747.799221\n",
      "make_peugot              -1393.751458\n",
      "make_mercury             -1339.684017\n",
      "make_subaru              -1134.710485\n",
      "make_toyota              -1086.907346\n",
      "make_nissan              -1068.960651\n",
      "fuel-system_mfi           -936.617172\n",
      "make_plymouth             -840.376165\n",
      "make_mitsubishi           -838.730415\n",
      "drive-wheels_fwd          -694.446584\n",
      "fuel-system_spdi          -622.638900\n",
      "body-style_wagon          -584.714963\n",
      "engine-type_l             -522.392679\n",
      "make_dodge                -495.085290\n",
      "aspiration_std            -410.795297\n",
      "body-style_hatchback      -385.984494\n",
      "make_volkswagen           -350.762977\n",
      "fuel-system_2bbl          -293.022982\n",
      "fuel-type_gas             -292.536961\n",
      "drive-wheels_4wd          -262.318409\n",
      "engine-type_dohc          -241.082404\n",
      "fuel-system_1bbl          -200.146230\n",
      "stroke                    -183.606453\n",
      "make_volvo                -160.922847\n",
      "make_mazda                 -92.705443\n",
      "make_alfa-romero           -88.759098\n",
      "num-of-doors_four          -72.104526\n",
      "city-mpg                   -47.167632\n",
      "highway-mpg                -38.948064\n",
      "                             ...     \n",
      "compression-ratio            7.516776\n",
      "height                      14.233382\n",
      "horsepower                  19.974885\n",
      "engine-size                 25.492384\n",
      "length                      29.544648\n",
      "wheel-base                  56.585625\n",
      "engine-type_ohcf            76.707481\n",
      "engine-type_ohcv            78.478491\n",
      "fuel-system_spfi           118.058839\n",
      "num-of-doors_two           146.951472\n",
      "engine-type_ohc            153.703471\n",
      "body-style_sedan           170.071897\n",
      "fuel-system_idi            292.536961\n",
      "fuel-type_diesel           292.536961\n",
      "width                      349.610638\n",
      "fuel-system_mpfi           357.484424\n",
      "aspiration_turbo           410.795297\n",
      "bore                       529.815929\n",
      "make_jaguar                622.748660\n",
      "body-style_hardtop         662.694102\n",
      "drive-wheels_rwd           760.905710\n",
      "num-of-cylinders           910.886349\n",
      "make_saab                 1044.714635\n",
      "make_audi                 1499.499030\n",
      "make_chevrolet            1700.637886\n",
      "body-style_convertible    2226.995273\n",
      "make_porsche              3836.566842\n",
      "engine-location_rear      3919.992407\n",
      "make_bmw                  5188.333576\n",
      "make_mercedes-benz        5438.343299\n",
      "Length: 68, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(ridge_model.coef_, predictors).sort_values()\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = ridge_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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HjDH/YYw5bIz5Y3f9XmPMaWPME+7HfnfdGGP+1hhzwhjzpDHmhrJz3WWMOe5+3FW2/iJj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\nYezYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c21b64e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (15,6)\n",
    "\n",
    "plt.plot(y_predict, label = 'Predicted')\n",
    "plt.plot(y_test.values, label = 'Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get R-square value for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92153224600271433"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsquare = ridge_model.score(x_test, y_test)\n",
    "rsquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2359.901793608148"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(ridge_model_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}