IBM_Simple_Linear Regression.ipynb

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   "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": "markdown",
   "metadata": {
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   },
   "source": [
    "## Reading the data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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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": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
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   "outputs": [
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       "  <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",
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       "  <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot features\n",
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot features against emission\n",
    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split training and test\n",
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]\n",
    "msk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Simple Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.65868694]]\n",
      "Intercept:  [126.22159095]\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": [
    "we can plot the fit line over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\n",
    "\n",
    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
    "    - Mean absolute error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
    "    - Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean absolute error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
    "    - Root Mean Squared Error (RMSE).\n",
    "    - R-squared is not error, but is a popular metric for accuracy of your model. It represents how close the data are to the fitted regression line. The higher the R-squared, the better the model fits your data. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.75\n",
      "Residual sum of squares (MSE): 849.43\n",
      "R2-score: 0.70\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_ = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y_ , test_y) )"
   ]
  }
 ],
 "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"
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  "widgets": {
   "state": {},
   "version": "1.1.2"
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 },
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}