Basics-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Introduction To The Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.options.display.max_columns = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</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>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</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>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</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>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</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>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  length  width  \\\n",
       "0  convertible          rwd           front        88.6   168.8   64.1   \n",
       "1  convertible          rwd           front        88.6   168.8   64.1   \n",
       "2    hatchback          rwd           front        94.5   171.2   65.5   \n",
       "3        sedan          fwd           front        99.8   176.6   66.2   \n",
       "4        sedan          4wd           front        99.4   176.6   66.4   \n",
       "\n",
       "   height  curb-weight engine-type num-of-cylinders  engine-size fuel-system  \\\n",
       "0    48.8         2548        dohc             four          130        mpfi   \n",
       "1    48.8         2548        dohc             four          130        mpfi   \n",
       "2    52.4         2823        ohcv              six          152        mpfi   \n",
       "3    54.3         2337         ohc             four          109        mpfi   \n",
       "4    54.3         2824         ohc             five          136        mpfi   \n",
       "\n",
       "   bore stroke  compression-rate horsepower peak-rpm  city-mpg  highway-mpg  \\\n",
       "0  3.47   2.68               9.0        111     5000        21           27   \n",
       "1  3.47   2.68               9.0        111     5000        21           27   \n",
       "2  2.68   3.47               9.0        154     5000        19           26   \n",
       "3  3.19   3.40              10.0        102     5500        24           30   \n",
       "4  3.19   3.40               8.0        115     5500        18           22   \n",
       "\n",
       "   price  \n",
       "0  13495  \n",
       "1  16500  \n",
       "2  16500  \n",
       "3  13950  \n",
       "4  17450  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', \n",
    "        'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', \n",
    "        'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-rate', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
    "cars = pd.read_csv('imports-85.data', names=cols)\n",
    "\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      "symboling            205 non-null int64\n",
      "normalized-losses    205 non-null object\n",
      "make                 205 non-null object\n",
      "fuel-type            205 non-null object\n",
      "aspiration           205 non-null object\n",
      "num-of-doors         205 non-null object\n",
      "body-style           205 non-null object\n",
      "drive-wheels         205 non-null object\n",
      "engine-location      205 non-null object\n",
      "wheel-base           205 non-null float64\n",
      "length               205 non-null float64\n",
      "width                205 non-null float64\n",
      "height               205 non-null float64\n",
      "curb-weight          205 non-null int64\n",
      "engine-type          205 non-null object\n",
      "num-of-cylinders     205 non-null object\n",
      "engine-size          205 non-null int64\n",
      "fuel-system          205 non-null object\n",
      "bore                 205 non-null object\n",
      "stroke               205 non-null object\n",
      "compression-rate     205 non-null float64\n",
      "horsepower           205 non-null object\n",
      "peak-rpm             205 non-null object\n",
      "city-mpg             205 non-null int64\n",
      "highway-mpg          205 non-null int64\n",
      "price                205 non-null object\n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking with columns needs to be changed to numeric value\n",
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#replacing the '?' values for NaN\n",
    "cars = cars.replace('?',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Selecting only the columns with continuous values from - https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names\n",
    "continuous_values_cols = ['normalized-losses', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-size', 'bore', 'stroke', 'compression-rate', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
    "numeric_cars = cars[continuous_values_cols].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.00</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.00</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164.0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.00</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99.8</td>\n",
       "      <td>177.3</td>\n",
       "      <td>66.3</td>\n",
       "      <td>53.1</td>\n",
       "      <td>2507.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>158.0</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2844.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2954.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>158.0</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>140.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3053.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>7.00</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>192.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>192.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>188.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>20970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>188.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3055.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3230.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>103.5</td>\n",
       "      <td>193.8</td>\n",
       "      <td>67.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>3380.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>3505.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.39</td>\n",
       "      <td>8.00</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>121.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>53.2</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.50</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>98.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>155.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>81.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>158.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>118.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.41</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>118.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>1876.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>118.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>2128.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>148.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>148.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>148.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.23</td>\n",
       "      <td>9.40</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>148.0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>50.6</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.60</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>110.0</td>\n",
       "      <td>103.3</td>\n",
       "      <td>174.6</td>\n",
       "      <td>64.6</td>\n",
       "      <td>59.8</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>8.50</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>145.0</td>\n",
       "      <td>95.9</td>\n",
       "      <td>173.2</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.90</td>\n",
       "      <td>7.00</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12964.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>65.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2414.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>65.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2414.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>65.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>175.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2458.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.54</td>\n",
       "      <td>8.70</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>197.0</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>197.0</td>\n",
       "      <td>102.9</td>\n",
       "      <td>183.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.30</td>\n",
       "      <td>161.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>90.0</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3131.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>156.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>122.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>122.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>94.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>94.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>94.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2275.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>94.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>94.0</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.5</td>\n",
       "      <td>159.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>256.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>165.7</td>\n",
       "      <td>64.0</td>\n",
       "      <td>51.4</td>\n",
       "      <td>2221.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.50</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>180.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2579.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100.4</td>\n",
       "      <td>183.1</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.1</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>103.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2912.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>74.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3034.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>103.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>74.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>103.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>3045.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>74.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>57.5</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>162.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>95.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>95.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.70</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>95.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.80</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>95.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.00</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>95.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     normalized-losses  wheel-base  length  width  height  curb-weight  \\\n",
       "0                  NaN        88.6   168.8   64.1    48.8       2548.0   \n",
       "1                  NaN        88.6   168.8   64.1    48.8       2548.0   \n",
       "2                  NaN        94.5   171.2   65.5    52.4       2823.0   \n",
       "3                164.0        99.8   176.6   66.2    54.3       2337.0   \n",
       "4                164.0        99.4   176.6   66.4    54.3       2824.0   \n",
       "5                  NaN        99.8   177.3   66.3    53.1       2507.0   \n",
       "6                158.0       105.8   192.7   71.4    55.7       2844.0   \n",
       "7                  NaN       105.8   192.7   71.4    55.7       2954.0   \n",
       "8                158.0       105.8   192.7   71.4    55.9       3086.0   \n",
       "9                  NaN        99.5   178.2   67.9    52.0       3053.0   \n",
       "10               192.0       101.2   176.8   64.8    54.3       2395.0   \n",
       "11               192.0       101.2   176.8   64.8    54.3       2395.0   \n",
       "12               188.0       101.2   176.8   64.8    54.3       2710.0   \n",
       "13               188.0       101.2   176.8   64.8    54.3       2765.0   \n",
       "14                 NaN       103.5   189.0   66.9    55.7       3055.0   \n",
       "15                 NaN       103.5   189.0   66.9    55.7       3230.0   \n",
       "16                 NaN       103.5   193.8   67.9    53.7       3380.0   \n",
       "17                 NaN       110.0   197.0   70.9    56.3       3505.0   \n",
       "18               121.0        88.4   141.1   60.3    53.2       1488.0   \n",
       "19                98.0        94.5   155.9   63.6    52.0       1874.0   \n",
       "20                81.0        94.5   158.8   63.6    52.0       1909.0   \n",
       "21               118.0        93.7   157.3   63.8    50.8       1876.0   \n",
       "22               118.0        93.7   157.3   63.8    50.8       1876.0   \n",
       "23               118.0        93.7   157.3   63.8    50.8       2128.0   \n",
       "24               148.0        93.7   157.3   63.8    50.6       1967.0   \n",
       "25               148.0        93.7   157.3   63.8    50.6       1989.0   \n",
       "26               148.0        93.7   157.3   63.8    50.6       1989.0   \n",
       "27               148.0        93.7   157.3   63.8    50.6       2191.0   \n",
       "28               110.0       103.3   174.6   64.6    59.8       2535.0   \n",
       "29               145.0        95.9   173.2   66.3    50.2       2811.0   \n",
       "..                 ...         ...     ...    ...     ...          ...   \n",
       "175               65.0       102.4   175.6   66.5    53.9       2414.0   \n",
       "176               65.0       102.4   175.6   66.5    54.9       2414.0   \n",
       "177               65.0       102.4   175.6   66.5    53.9       2458.0   \n",
       "178              197.0       102.9   183.5   67.7    52.0       2976.0   \n",
       "179              197.0       102.9   183.5   67.7    52.0       3016.0   \n",
       "180               90.0       104.5   187.8   66.5    54.1       3131.0   \n",
       "181                NaN       104.5   187.8   66.5    54.1       3151.0   \n",
       "182              122.0        97.3   171.7   65.5    55.7       2261.0   \n",
       "183              122.0        97.3   171.7   65.5    55.7       2209.0   \n",
       "184               94.0        97.3   171.7   65.5    55.7       2264.0   \n",
       "185               94.0        97.3   171.7   65.5    55.7       2212.0   \n",
       "186               94.0        97.3   171.7   65.5    55.7       2275.0   \n",
       "187               94.0        97.3   171.7   65.5    55.7       2319.0   \n",
       "188               94.0        97.3   171.7   65.5    55.7       2300.0   \n",
       "189                NaN        94.5   159.3   64.2    55.6       2254.0   \n",
       "190              256.0        94.5   165.7   64.0    51.4       2221.0   \n",
       "191                NaN       100.4   180.2   66.9    55.1       2661.0   \n",
       "192                NaN       100.4   180.2   66.9    55.1       2579.0   \n",
       "193                NaN       100.4   183.1   66.9    55.1       2563.0   \n",
       "194              103.0       104.3   188.8   67.2    56.2       2912.0   \n",
       "195               74.0       104.3   188.8   67.2    57.5       3034.0   \n",
       "196              103.0       104.3   188.8   67.2    56.2       2935.0   \n",
       "197               74.0       104.3   188.8   67.2    57.5       3042.0   \n",
       "198              103.0       104.3   188.8   67.2    56.2       3045.0   \n",
       "199               74.0       104.3   188.8   67.2    57.5       3157.0   \n",
       "200               95.0       109.1   188.8   68.9    55.5       2952.0   \n",
       "201               95.0       109.1   188.8   68.8    55.5       3049.0   \n",
       "202               95.0       109.1   188.8   68.9    55.5       3012.0   \n",
       "203               95.0       109.1   188.8   68.9    55.5       3217.0   \n",
       "204               95.0       109.1   188.8   68.9    55.5       3062.0   \n",
       "\n",
       "     engine-size  bore  stroke  compression-rate  horsepower  peak-rpm  \\\n",
       "0          130.0  3.47    2.68              9.00       111.0    5000.0   \n",
       "1          130.0  3.47    2.68              9.00       111.0    5000.0   \n",
       "2          152.0  2.68    3.47              9.00       154.0    5000.0   \n",
       "3          109.0  3.19    3.40             10.00       102.0    5500.0   \n",
       "4          136.0  3.19    3.40              8.00       115.0    5500.0   \n",
       "5          136.0  3.19    3.40              8.50       110.0    5500.0   \n",
       "6          136.0  3.19    3.40              8.50       110.0    5500.0   \n",
       "7          136.0  3.19    3.40              8.50       110.0    5500.0   \n",
       "8          131.0  3.13    3.40              8.30       140.0    5500.0   \n",
       "9          131.0  3.13    3.40              7.00       160.0    5500.0   \n",
       "10         108.0  3.50    2.80              8.80       101.0    5800.0   \n",
       "11         108.0  3.50    2.80              8.80       101.0    5800.0   \n",
       "12         164.0  3.31    3.19              9.00       121.0    4250.0   \n",
       "13         164.0  3.31    3.19              9.00       121.0    4250.0   \n",
       "14         164.0  3.31    3.19              9.00       121.0    4250.0   \n",
       "15         209.0  3.62    3.39              8.00       182.0    5400.0   \n",
       "16         209.0  3.62    3.39              8.00       182.0    5400.0   \n",
       "17         209.0  3.62    3.39              8.00       182.0    5400.0   \n",
       "18          61.0  2.91    3.03              9.50        48.0    5100.0   \n",
       "19          90.0  3.03    3.11              9.60        70.0    5400.0   \n",
       "20          90.0  3.03    3.11              9.60        70.0    5400.0   \n",
       "21          90.0  2.97    3.23              9.41        68.0    5500.0   \n",
       "22          90.0  2.97    3.23              9.40        68.0    5500.0   \n",
       "23          98.0  3.03    3.39              7.60       102.0    5500.0   \n",
       "24          90.0  2.97    3.23              9.40        68.0    5500.0   \n",
       "25          90.0  2.97    3.23              9.40        68.0    5500.0   \n",
       "26          90.0  2.97    3.23              9.40        68.0    5500.0   \n",
       "27          98.0  3.03    3.39              7.60       102.0    5500.0   \n",
       "28         122.0  3.34    3.46              8.50        88.0    5000.0   \n",
       "29         156.0  3.60    3.90              7.00       145.0    5000.0   \n",
       "..           ...   ...     ...               ...         ...       ...   \n",
       "175        122.0  3.31    3.54              8.70        92.0    4200.0   \n",
       "176        122.0  3.31    3.54              8.70        92.0    4200.0   \n",
       "177        122.0  3.31    3.54              8.70        92.0    4200.0   \n",
       "178        171.0  3.27    3.35              9.30       161.0    5200.0   \n",
       "179        171.0  3.27    3.35              9.30       161.0    5200.0   \n",
       "180        171.0  3.27    3.35              9.20       156.0    5200.0   \n",
       "181        161.0  3.27    3.35              9.20       156.0    5200.0   \n",
       "182         97.0  3.01    3.40             23.00        52.0    4800.0   \n",
       "183        109.0  3.19    3.40              9.00        85.0    5250.0   \n",
       "184         97.0  3.01    3.40             23.00        52.0    4800.0   \n",
       "185        109.0  3.19    3.40              9.00        85.0    5250.0   \n",
       "186        109.0  3.19    3.40              9.00        85.0    5250.0   \n",
       "187         97.0  3.01    3.40             23.00        68.0    4500.0   \n",
       "188        109.0  3.19    3.40             10.00       100.0    5500.0   \n",
       "189        109.0  3.19    3.40              8.50        90.0    5500.0   \n",
       "190        109.0  3.19    3.40              8.50        90.0    5500.0   \n",
       "191        136.0  3.19    3.40              8.50       110.0    5500.0   \n",
       "192         97.0  3.01    3.40             23.00        68.0    4500.0   \n",
       "193        109.0  3.19    3.40              9.00        88.0    5500.0   \n",
       "194        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "195        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "196        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "197        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "198        130.0  3.62    3.15              7.50       162.0    5100.0   \n",
       "199        130.0  3.62    3.15              7.50       162.0    5100.0   \n",
       "200        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "201        141.0  3.78    3.15              8.70       160.0    5300.0   \n",
       "202        173.0  3.58    2.87              8.80       134.0    5500.0   \n",
       "203        145.0  3.01    3.40             23.00       106.0    4800.0   \n",
       "204        141.0  3.78    3.15              9.50       114.0    5400.0   \n",
       "\n",
       "     city-mpg  highway-mpg    price  \n",
       "0        21.0         27.0  13495.0  \n",
       "1        21.0         27.0  16500.0  \n",
       "2        19.0         26.0  16500.0  \n",
       "3        24.0         30.0  13950.0  \n",
       "4        18.0         22.0  17450.0  \n",
       "5        19.0         25.0  15250.0  \n",
       "6        19.0         25.0  17710.0  \n",
       "7        19.0         25.0  18920.0  \n",
       "8        17.0         20.0  23875.0  \n",
       "9        16.0         22.0      NaN  \n",
       "10       23.0         29.0  16430.0  \n",
       "11       23.0         29.0  16925.0  \n",
       "12       21.0         28.0  20970.0  \n",
       "13       21.0         28.0  21105.0  \n",
       "14       20.0         25.0  24565.0  \n",
       "15       16.0         22.0  30760.0  \n",
       "16       16.0         22.0  41315.0  \n",
       "17       15.0         20.0  36880.0  \n",
       "18       47.0         53.0   5151.0  \n",
       "19       38.0         43.0   6295.0  \n",
       "20       38.0         43.0   6575.0  \n",
       "21       37.0         41.0   5572.0  \n",
       "22       31.0         38.0   6377.0  \n",
       "23       24.0         30.0   7957.0  \n",
       "24       31.0         38.0   6229.0  \n",
       "25       31.0         38.0   6692.0  \n",
       "26       31.0         38.0   7609.0  \n",
       "27       24.0         30.0   8558.0  \n",
       "28       24.0         30.0   8921.0  \n",
       "29       19.0         24.0  12964.0  \n",
       "..        ...          ...      ...  \n",
       "175      27.0         32.0   9988.0  \n",
       "176      27.0         32.0  10898.0  \n",
       "177      27.0         32.0  11248.0  \n",
       "178      20.0         24.0  16558.0  \n",
       "179      19.0         24.0  15998.0  \n",
       "180      20.0         24.0  15690.0  \n",
       "181      19.0         24.0  15750.0  \n",
       "182      37.0         46.0   7775.0  \n",
       "183      27.0         34.0   7975.0  \n",
       "184      37.0         46.0   7995.0  \n",
       "185      27.0         34.0   8195.0  \n",
       "186      27.0         34.0   8495.0  \n",
       "187      37.0         42.0   9495.0  \n",
       "188      26.0         32.0   9995.0  \n",
       "189      24.0         29.0  11595.0  \n",
       "190      24.0         29.0   9980.0  \n",
       "191      19.0         24.0  13295.0  \n",
       "192      33.0         38.0  13845.0  \n",
       "193      25.0         31.0  12290.0  \n",
       "194      23.0         28.0  12940.0  \n",
       "195      23.0         28.0  13415.0  \n",
       "196      24.0         28.0  15985.0  \n",
       "197      24.0         28.0  16515.0  \n",
       "198      17.0         22.0  18420.0  \n",
       "199      17.0         22.0  18950.0  \n",
       "200      23.0         28.0  16845.0  \n",
       "201      19.0         25.0  19045.0  \n",
       "202      18.0         23.0  21485.0  \n",
       "203      26.0         27.0  22470.0  \n",
       "204      19.0         25.0  22625.0  \n",
       "\n",
       "[205 rows x 15 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing all numerical values to 'float'\n",
    "numeric_cars.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    41\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-size           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-rate      0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking naN values in df\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    37\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-size           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-rate      0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Because `price` is the column we want to predict, let's remove any rows with missing `price` values.\n",
    "numeric_cars = numeric_cars.dropna(subset=['price'])\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-size          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-rate     0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing missing values in other columns using column means.\n",
    "numeric_cars = numeric_cars.fillna(numeric_cars.mean())\n",
    "\n",
    "#confirming tha there's no more missing values\n",
    "numeric_cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-rate</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.298429</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.324786</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.411171</td>\n",
       "      <td>0.260377</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.290476</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.298429</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.324786</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.411171</td>\n",
       "      <td>0.260377</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.290476</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.298429</td>\n",
       "      <td>0.230321</td>\n",
       "      <td>0.449254</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.383333</td>\n",
       "      <td>0.517843</td>\n",
       "      <td>0.343396</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.495327</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.518325</td>\n",
       "      <td>0.384840</td>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.504274</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.329325</td>\n",
       "      <td>0.181132</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.252336</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.518325</td>\n",
       "      <td>0.373178</td>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.521368</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.518231</td>\n",
       "      <td>0.283019</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.313084</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   normalized-losses  wheel-base    length     width    height  curb-weight  \\\n",
       "0           0.298429    0.058309  0.413433  0.324786  0.083333     0.411171   \n",
       "1           0.298429    0.058309  0.413433  0.324786  0.083333     0.411171   \n",
       "2           0.298429    0.230321  0.449254  0.444444  0.383333     0.517843   \n",
       "3           0.518325    0.384840  0.529851  0.504274  0.541667     0.329325   \n",
       "4           0.518325    0.373178  0.529851  0.521368  0.541667     0.518231   \n",
       "\n",
       "   engine-size      bore    stroke  compression-rate  horsepower  peak-rpm  \\\n",
       "0     0.260377  0.664286  0.290476            0.1250    0.294393  0.346939   \n",
       "1     0.260377  0.664286  0.290476            0.1250    0.294393  0.346939   \n",
       "2     0.343396  0.100000  0.666667            0.1250    0.495327  0.346939   \n",
       "3     0.181132  0.464286  0.633333            0.1875    0.252336  0.551020   \n",
       "4     0.283019  0.464286  0.633333            0.0625    0.313084  0.551020   \n",
       "\n",
       "   city-mpg  highway-mpg    price  \n",
       "0  0.222222     0.289474  13495.0  \n",
       "1  0.222222     0.289474  16500.0  \n",
       "2  0.166667     0.263158  16500.0  \n",
       "3  0.305556     0.368421  13950.0  \n",
       "4  0.138889     0.157895  17450.0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize all columnns to range from 0 to 1 except the target column ('price').\n",
    "price_col = numeric_cars['price']\n",
    "numeric_cars = (numeric_cars - numeric_cars.min())/(numeric_cars.max() - numeric_cars.min())\n",
    "numeric_cars['price'] = price_col\n",
    "numeric_cars.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a function, named knn_train_test() that encapsulates the training and simple validation process. This function should have 3 parameters -- training column name, target column name, and the dataframe object.\n",
    "- This function should split the data set into a training and test set.\n",
    "- Then, it should instantiate the KNeighborsRegressor class, fit the model on the training set, and make predictions on the test set.\n",
    "- Finally, it should calculate the RMSE and return that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "engine-size          3238.462830\n",
       "horsepower           4037.037713\n",
       "curb-weight          4401.118255\n",
       "highway-mpg          4630.026799\n",
       "width                4704.482590\n",
       "city-mpg             4766.422505\n",
       "length               5427.200961\n",
       "wheel-base           5461.553998\n",
       "compression-rate     6610.812153\n",
       "bore                 6780.627785\n",
       "normalized-losses    7330.197653\n",
       "peak-rpm             7697.459696\n",
       "stroke               8006.529545\n",
       "height               8144.441043\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def knn_train_test(train_col, target_col, df):\n",
    "    knn = KNeighborsRegressor()\n",
    "    np.random.seed(1)\n",
    "        \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    # Fit a KNN model using default k value.\n",
    "    knn.fit(train_df[[train_col]], train_df[target_col])\n",
    "    \n",
    "    # Make predictions using model.\n",
    "    predicted_labels = knn.predict(test_df[[train_col]])\n",
    "\n",
    "    # Calculate and return RMSE.\n",
    "    mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "    rmse = np.sqrt(mse)\n",
    "    return rmse\n",
    "\n",
    "rmse_results = {}\n",
    "train_cols = numeric_cars.columns.drop('price')\n",
    "\n",
    "# For each column (minus `price`), train a model, return RMSE value\n",
    "# and add to the dictionary `rmse_results`.\n",
    "for col in train_cols:\n",
    "    rmse_val = knn_train_test(col, 'price', numeric_cars)\n",
    "    rmse_results[col] = rmse_val\n",
    "\n",
    "# Create a Series object from the dictionary so \n",
    "# we can easily view the results, sort, etc\n",
    "rmse_results_series = pd.Series(rmse_results)\n",
    "rmse_results_series.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "finding the value RMSE for each k for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bore': {1: 8602.58848450066,\n",
       "  3: 6984.239489480916,\n",
       "  5: 6780.627784685976,\n",
       "  7: 6878.097965921532,\n",
       "  9: 6866.808502038413},\n",
       " 'city-mpg': {1: 5901.143574354764,\n",
       "  3: 4646.746408727155,\n",
       "  5: 4766.422505090134,\n",
       "  7: 5232.523034167316,\n",
       "  9: 5465.209492527533},\n",
       " 'compression-rate': {1: 8087.205346523092,\n",
       "  3: 7375.063685578359,\n",
       "  5: 6610.812153159129,\n",
       "  7: 6732.801282941515,\n",
       "  9: 7024.485525463435},\n",
       " 'curb-weight': {1: 5264.290230758878,\n",
       "  3: 5022.318011757233,\n",
       "  5: 4401.118254793124,\n",
       "  7: 4330.608104418053,\n",
       "  9: 4632.044474454401},\n",
       " 'engine-size': {1: 3258.4861059962027,\n",
       "  3: 2840.562805643501,\n",
       "  5: 3238.4628296477176,\n",
       "  7: 3563.086774256415,\n",
       "  9: 3831.8244149840766},\n",
       " 'height': {1: 8904.04645636071,\n",
       "  3: 8277.609643045525,\n",
       "  5: 8144.441042663747,\n",
       "  7: 7679.598124393773,\n",
       "  9: 7811.03606291223},\n",
       " 'highway-mpg': {1: 6025.594966720739,\n",
       "  3: 4617.305019788554,\n",
       "  5: 4630.026798588056,\n",
       "  7: 4796.061440186946,\n",
       "  9: 5278.358056953987},\n",
       " 'horsepower': {1: 4170.054848037801,\n",
       "  3: 4020.8492630885394,\n",
       "  5: 4037.0377131537603,\n",
       "  7: 4353.811860277134,\n",
       "  9: 4515.135617419103},\n",
       " 'length': {1: 4628.45550121557,\n",
       "  3: 5129.8358210721635,\n",
       "  5: 5427.2009608367125,\n",
       "  7: 5313.427720847974,\n",
       "  9: 5383.054514833446},\n",
       " 'normalized-losses': {1: 7846.750605148984,\n",
       "  3: 7500.5698123109905,\n",
       "  5: 7330.197653434445,\n",
       "  7: 7756.421586234123,\n",
       "  9: 7688.096096891432},\n",
       " 'peak-rpm': {1: 9511.480067750124,\n",
       "  3: 8537.550899973421,\n",
       "  5: 7697.4596964334805,\n",
       "  7: 7510.294160083481,\n",
       "  9: 7340.041341263401},\n",
       " 'stroke': {1: 9116.495955406906,\n",
       "  3: 7338.68466990294,\n",
       "  5: 8006.529544647101,\n",
       "  7: 7803.937796804327,\n",
       "  9: 7735.554366079291},\n",
       " 'wheel-base': {1: 4493.734068810494,\n",
       "  3: 5120.161506064513,\n",
       "  5: 5461.553997873057,\n",
       "  7: 5448.1070513823315,\n",
       "  9: 5738.405685192312},\n",
       " 'width': {1: 4559.257297950061,\n",
       "  3: 4606.413692169901,\n",
       "  5: 4704.482589704386,\n",
       "  7: 4571.485046194653,\n",
       "  9: 4652.914172067787}}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_col, target_col, df):\n",
    "    knn = KNeighborsRegressor()\n",
    "    np.random.seed(1)\n",
    "        \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    # Fit a KNN model using default k value.\n",
    "    knn.fit(train_df[[train_col]], train_df[target_col])\n",
    "    \n",
    "    k_values = [1,3,5,7,9]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[[train_col]], train_df[target_col])\n",
    "    \n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[[train_col]])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "\n",
    "# For each column (minus `price`), train a model, return RMSE value\n",
    "# and add to the dictionary `rmse_results`.\n",
    "train_cols = numeric_cars.columns.drop('price')\n",
    "for col in train_cols:\n",
    "    rmse_val = knn_train_test(col, 'price', numeric_cars)\n",
    "    k_rmse_results[col] = rmse_val\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEPCAYAAACHuClZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIZkQBEEQGUMyIQiCIDKGZEIQBEFkzP8H2diyabn3L7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57dcb67518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "for k,v in k_rmse_results.items():\n",
    "    x = list(v.keys())\n",
    "    y = list(v.values())\n",
    "    \n",
    "    plt.plot(x,y)\n",
    "    plt.xlabel('k value')\n",
    "    plt.ylabel('RMSE') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will compute average RMSE across different `k` values for each feature and sort each value in an descending order. We can get this way a top list with the 'best features' from our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'height': 8163.346265875196, 'bore': 7222.472445325499, 'curb-weight': 4730.075815236338, 'city-mpg': 5202.40900297338, 'peak-rpm': 8119.365233100782, 'highway-mpg': 5069.469256447656, 'wheel-base': 5252.3924618645415, 'normalized-losses': 7624.407150803995, 'engine-size': 3346.4845861055824, 'stroke': 8000.240466568112, 'length': 5176.394903761173, 'width': 4618.910559617357, 'horsepower': 4219.377860395267, 'compression-rate': 7166.0735987331045}\n",
      "\n",
      "\n",
      "engine-size          3346.484586\n",
      "horsepower           4219.377860\n",
      "width                4618.910560\n",
      "curb-weight          4730.075815\n",
      "highway-mpg          5069.469256\n",
      "length               5176.394904\n",
      "city-mpg             5202.409003\n",
      "wheel-base           5252.392462\n",
      "compression-rate     7166.073599\n",
      "bore                 7222.472445\n",
      "normalized-losses    7624.407151\n",
      "stroke               8000.240467\n",
      "peak-rpm             8119.365233\n",
      "height               8163.346266\n",
      "dtype: float64\n",
      "\n",
      "\n",
      "Index(['engine-size', 'horsepower', 'width', 'curb-weight', 'highway-mpg',\n",
      "       'length', 'city-mpg', 'wheel-base', 'compression-rate', 'bore',\n",
      "       'normalized-losses', 'stroke', 'peak-rpm', 'height'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "feature_avg_rmse = {}\n",
    "for k,v in k_rmse_results.items():\n",
    "    avg_rmse = np.mean(list(v.values()))\n",
    "    feature_avg_rmse[k] = avg_rmse\n",
    "\n",
    "print(feature_avg_rmse)\n",
    "print('\\n')\n",
    "\n",
    "sorted_series_avg_rmse = pd.Series(feature_avg_rmse).sort_values()\n",
    "sorted_features = sorted_series_avg_rmse.index\n",
    "\n",
    "print(sorted_series_avg_rmse)\n",
    "print('\\n')\n",
    "\n",
    "print(sorted_features)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will modify the function we wrote to work with multiple columns, considering always the best features. This time with a constant k =5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2 best features': {5: 2949.8817277180374},\n",
       " '3 best features': {5: 3580.7376651928435},\n",
       " '4 best features': {5: 3487.340917327035},\n",
       " '5 best features': {5: 3410.2170133901805},\n",
       " '6 best features': {5: 3478.510890118539}}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_cols, target_col, df):\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    k_values = [5]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[train_cols], train_df[target_col])\n",
    "\n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[train_cols])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "# NOw let's find the best k features from this model using the default k value.\n",
    "\n",
    "for nr_best_feats in range(2,7):\n",
    "    k_rmse_results['{} best features'.format(nr_best_feats)] = knn_train_test(\n",
    "        sorted_features[:nr_best_feats],'price',numeric_cars)\n",
    "\n",
    "#sorted_features[:nr_best_feats] will consider the best k columns\n",
    "#from the data train set\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to optimize the model. We will vary the hyperparameter value from 1 to 25 and plot the resulting RMSE values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2 best features': {1: 2783.6204237227344,\n",
       "  2: 2657.7963807419765,\n",
       "  3: 2792.586573031673,\n",
       "  4: 2891.5329686923255,\n",
       "  5: 2949.8817277180374,\n",
       "  6: 3096.402601694776,\n",
       "  7: 3164.681969020496,\n",
       "  8: 3413.228359192009,\n",
       "  9: 3748.6716603306486,\n",
       "  10: 4080.7125057341937,\n",
       "  11: 4215.6372280600335,\n",
       "  12: 4275.421524277872,\n",
       "  13: 4373.901683035496,\n",
       "  14: 4424.285137239815,\n",
       "  15: 4539.505493095937,\n",
       "  16: 4667.307671446768,\n",
       "  17: 4729.605305844226,\n",
       "  18: 4790.556632159094,\n",
       "  19: 4824.3866193292615,\n",
       "  20: 4840.850914693829,\n",
       "  21: 4837.429062000271,\n",
       "  22: 4831.16988267597,\n",
       "  23: 4861.679492959275,\n",
       "  24: 4903.346008862579},\n",
       " '3 best features': {1: 3399.8148100410203,\n",
       "  2: 3497.191103423058,\n",
       "  3: 3333.6966577570593,\n",
       "  4: 3355.8842294742026,\n",
       "  5: 3580.7376651928435,\n",
       "  6: 3732.943016673517,\n",
       "  7: 3639.9439408462786,\n",
       "  8: 3747.4209132113137,\n",
       "  9: 3986.593913133887,\n",
       "  10: 4005.354888715163,\n",
       "  11: 4121.687230061635,\n",
       "  12: 4255.700651624227,\n",
       "  13: 4328.476829895253,\n",
       "  14: 4332.216494947217,\n",
       "  15: 4388.225713011904,\n",
       "  16: 4408.838883583756,\n",
       "  17: 4404.781029718083,\n",
       "  18: 4447.577705091259,\n",
       "  19: 4537.049753345422,\n",
       "  20: 4592.444230865941,\n",
       "  21: 4636.731219491763,\n",
       "  22: 4721.248544133379,\n",
       "  23: 4787.943506313775,\n",
       "  24: 4802.894378990491},\n",
       " '4 best features': {1: 2952.725686581471,\n",
       "  2: 3131.704952720018,\n",
       "  3: 3129.692821910155,\n",
       "  4: 3241.4320776448717,\n",
       "  5: 3487.340917327035,\n",
       "  6: 3637.0381471429987,\n",
       "  7: 3606.195077860286,\n",
       "  8: 3809.9307026308247,\n",
       "  9: 3875.274902378068,\n",
       "  10: 3997.1583055842293,\n",
       "  11: 4162.564050411074,\n",
       "  12: 4289.486490995821,\n",
       "  13: 4368.061602779942,\n",
       "  14: 4416.304772968801,\n",
       "  15: 4434.013914355171,\n",
       "  16: 4441.4634909198785,\n",
       "  17: 4512.996303789127,\n",
       "  18: 4523.575629742228,\n",
       "  19: 4534.834065236792,\n",
       "  20: 4620.211598150367,\n",
       "  21: 4688.356509517293,\n",
       "  22: 4731.46717779913,\n",
       "  23: 4763.535312989311,\n",
       "  24: 4751.601375872476},\n",
       " '5 best features': {1: 2824.7061233282866,\n",
       "  2: 2915.6731645496975,\n",
       "  3: 3012.4204546509704,\n",
       "  4: 3202.8876051367483,\n",
       "  5: 3410.2170133901805,\n",
       "  6: 3618.4509432660384,\n",
       "  7: 3622.6290209234803,\n",
       "  8: 3848.635835654326,\n",
       "  9: 3977.8149139381726,\n",
       "  10: 3994.8132211260104,\n",
       "  11: 4159.843526607947,\n",
       "  12: 4294.3389473154875,\n",
       "  13: 4380.848359486949,\n",
       "  14: 4466.368754416089,\n",
       "  15: 4522.420711094978,\n",
       "  16: 4536.427578452413,\n",
       "  17: 4587.098443664006,\n",
       "  18: 4622.107837952761,\n",
       "  19: 4612.890107622797,\n",
       "  20: 4632.693976139521,\n",
       "  21: 4712.917548435062,\n",
       "  22: 4676.301064518744,\n",
       "  23: 4691.189310956096,\n",
       "  24: 4755.990767231825}}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def knn_train_test(train_cols, target_col, df):\n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # Randomize order of rows in data frame.\n",
    "    shuffled_index = np.random.permutation(df.index)\n",
    "    rand_df = df.reindex(shuffled_index)\n",
    "\n",
    "    # Divide number of rows in half and round.\n",
    "    last_train_row = int(len(rand_df) / 2)\n",
    "    \n",
    "    # Select the first half and set as training set.\n",
    "    # Select the second half and set as test set.\n",
    "    train_df = rand_df.iloc[0:last_train_row]\n",
    "    test_df = rand_df.iloc[last_train_row:]\n",
    "    \n",
    "    k_values = [i for i in range(1, 25)]\n",
    "    k_rmses = {}\n",
    "    \n",
    "    for k in k_values:\n",
    "        # Fit model using k nearest neighbors.\n",
    "        knn = KNeighborsRegressor(n_neighbors=k)\n",
    "        knn.fit(train_df[train_cols], train_df[target_col])\n",
    "\n",
    "        # Make predictions using model.\n",
    "        predicted_labels = knn.predict(test_df[train_cols])\n",
    "\n",
    "        # Calculate and return RMSE.\n",
    "        mse = mean_squared_error(test_df[target_col], predicted_labels)\n",
    "        rmse = np.sqrt(mse)\n",
    "        \n",
    "        k_rmses[k] = rmse\n",
    "    return k_rmses\n",
    "\n",
    "k_rmse_results = {}\n",
    "\n",
    "for nr_best_feats in range(2,6):\n",
    "    k_rmse_results['{} best features'.format(nr_best_feats)] = knn_train_test(\n",
    "        sorted_features[:nr_best_feats],'price',numeric_cars)\n",
    "\n",
    "k_rmse_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f57deefddd8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57deefd208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k,v in k_rmse_results.items():\n",
    "    x = list(v.keys())\n",
    "    y = list(v.values())  \n",
    "    plt.plot(x,y, label=\"{}\".format(k))\n",
    "    \n",
    "plt.xlabel('k value')\n",
    "plt.ylabel('RMSE')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best k parameter for each model seems to be always between 2 and 5. The results within these ranges are the lowest error values."
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}