01_Regression_KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nbAgg\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "print(matplotlib.get_backend())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "3          AS6        Z                  12.7                  9.1   \n",
       "4          AS6        Z                  12.1                  8.7   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  \n",
       "3                  11.1                        25           255  \n",
       "4                  10.6                        27           244  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('FuelConsumptionCo2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>MODELYEAR</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['ENGINESIZE']], df['CO2EMISSIONS'], random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "for n, ax in zip([1,5,9], axes):\n",
    "    reg = KNeighborsRegressor(n_neighbors=n)\n",
    "    reg.fit(X_train, y_train)\n",
    "    ax.plot(X_train, y_train, '^', markersize=8)\n",
    "    ax.plot(X_test, y_test, 'v', markersize=8)\n",
    "    ax.set_title(\"{} neighbor(s)\\n train score: {:.2f} test score: {:.2f}\"\n",
    "                 .format(n, reg.score(X_train, y_train), reg.score(X_test, y_test)))\n",
    "    ax.set_xlabel(\"Feature: ENGINESIZE\")\n",
    "    ax.set_ylabel(\"Target: CO2EMISSIONS\")\n",
    "axes[0].legend([\"Model predictions\", \"Training data/target\",\n",
    "                \"Test data/target\"], loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}