{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-05-17 12:45:09--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 72629 (71K) [text/csv]\n",
      "Saving to: ‘FuelConsumption.csv’\n",
      "\n",
      "FuelConsumption.csv 100%[===================>]  70,93K   123KB/s    in 0,6s    \n",
      "\n",
      "2020-05-17 12:45:10 (123 KB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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(\"FuelConsumption.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.FUELCONSUMPTION_COMB,cdf.CO2EMISSIONS,color=\"blue\")\n",
    "plt.xlabel(\"FUEL CONSUMPTION\")\n",
    "plt.ylabel(\"CO2 EMISSION\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.ENGINESIZE,cdf.CO2EMISSIONS,color=\"red\")\n",
    "plt.xlabel(\"ENGINE SIZE\")\n",
    "plt.ylabel(\"CO2 EMISSION\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.CYLINDERS,cdf.CO2EMISSIONS,color=\"green\")\n",
    "plt.xlabel(\"CYLINDERS\")\n",
    "plt.ylabel(\"CO2 EMISSION\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toplam verinin %80'inin train olarak almak için oluşturduğumuz array. Bu bir maskeleme sağlar.\n",
    "msk = np.random.rand(len(df)) < 0.8 \n",
    "train = cdf[msk] # Eğitim verisi\n",
    "test = cdf[~msk] # Test verisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression\n",
    "\n",
    "Verilen tek bir bağımsız değişkene karşılık bağımlı bir değişkeni tahmin etmeye çalışacağımız bir model bulmaya çalışacağız. Buradaki örnek için ENGINESIZE ile CO2EMISSION arasındaki ilişkiyi inceleyeceğiz.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Train verisini üzerindeki dağılımı gösteren grafik***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE,train.CO2EMISSIONS,color=\"red\")\n",
    "plt.xlabel(\"Engine Size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Modeli eğitelim***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "regr=linear_model.LinearRegression()\n",
    "train_x=np.asanyarray(train[[\"ENGINESIZE\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.47167529]]\n",
      "Intercept:  [123.75836704]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit (train_x, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "print ('Intercept: ',regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelimizin çıktısı olan grafiği eğitim verisi ile birlikte grafik üzerinde gösterelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Değerlendirme***\n",
    "\n",
    "Şimdi ise üretmiş olduğumuz modelin ne kadar iyi olduğunu değerlendireceğiz. Bunun için aşağıdaki göstergeleri kullanabiliriz. \n",
    "\n",
    "* ***Mean Absolute Error (MAE):*** Gerçek değer ile tahminler arasındaki farkın mutlak değerinin ortalamasıdır.\n",
    "* ***Mean Squared Error (MSE):*** Gerçek değer ile tahminler arasındaki farkın karesinin ortalamasıdır. Bu MEA'dan daha popülerdir.Bunun değeri çıkan değerde büyük hataların, karelerin alınması dolayaısı ile  daha yüksek oranda etki yapmasıdır. \n",
    "* ***Root Mean Squared Error (RMSE):*** MSE değerinin kare köküdür.\n",
    "* ***R-square:*** Gerçek değere ne kadar yakın olduğunu ölçmek için kullanılır. Değer ne kadar büyükse tahmin o kadar iyidir. En büyük değer 1.0 olabilir. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 24.46\n",
      "MSE: 1017.71\n",
      "R2-score: 0.66\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x=np.asanyarray(test[[\"ENGINESIZE\"]])\n",
    "test_y=np.asanyarray(test[[\"CO2EMISSIONS\"]])\n",
    "test_y_hat=regr.predict(test_x)\n",
    "\n",
    "print(\"MAE: %.2f\" % np.mean(np.absolute(test_y_hat-test_y)))\n",
    "print(\"MSE: %.2f\" % np.mean((test_y_hat-test_y)**2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y_hat,test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yukarıdaki R2 score değeri kötü değil ama daha iyi olabilir bunun için farklı featurelar denenebilir yada daha fazla feature alınarak Multiple Regression Yapılabilir. Bir sonrakinde bu konuyu işleyeceğiz."
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}