ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 400, align = \"center\"></a>\n",
    "\n",
    "# <center>Simple Linear Regression</center>\n",
    "\n",
    "\n",
    "#### About this Notebook\n",
    "In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, Evaluate your model using test set, and finally use model to predict unknown value\n"
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   "source": [
    "### Importing Needed packages"
   ]
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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"
   ]
  },
  {
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   },
   "source": [
    "### Downloading Data\n",
    "To download the data, we will use !wget to download it from IBM Object Storage."
   ]
  },
  {
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     "text": [
      "--2019-02-15 07:09:12--  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)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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  --.-KB/s   in 0.04s  \n",
      "\n",
      "2019-02-15 07:09:12 (1.64 MB/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": "markdown",
   "metadata": {},
   "source": [
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
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   "source": [
    "\n",
    "## Understanding the Data\n",
    "\n",
    "### `FuelConsumption.csv`:\n",
    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
    "\n",
    "- **MODELYEAR** e.g. 2014\n",
    "- **MAKE** e.g. Acura\n",
    "- **MODEL** e.g. ILX\n",
    "- **VEHICLE CLASS** e.g. SUV\n",
    "- **ENGINE SIZE** e.g. 4.7\n",
    "- **CYLINDERS** e.g 6\n",
    "- **TRANSMISSION** e.g. A6\n",
    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
   ]
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   },
   "source": [
    "## Reading the data in"
   ]
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       "  <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",
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       "    <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",
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       "    <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",
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      "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  "
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     "execution_count": 3,
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     "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"
   ]
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   "source": [
    "### Data Exploration\n",
    "Lets first have a descriptive exploration on our data."
   ]
  },
  {
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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",
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       "    <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": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets select some features to explore more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <th>ENGINESIZE</th>\n",
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       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
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       "    <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": "markdown",
   "metadata": {},
   "source": [
    "we can plot each of these fearues:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXj/vhtI8304T9v2vpNXk7vfvzN+odL/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJdPz7fibaVduZmbN6/nAb/2hlR9oP/Rl1h2u6jEzKxmX+M3MxkivXBl3pMQv6VBJD0haJumMTqzDrNf4uLd+0fYSv6QtgYuBQ4AVwE8kLYyIn7V7XWa9oh+P+2Lps1bHe2XSbEm8uJ/6+R5VJ6p6DgCWRcSvACR9DZgF9OwJYNYGbTvu3ZLNOk0R0d4FSkcDh0bE+/L48cDrIuKUqvnmAnPz6L7AA23NSPtMAh7tdiZ6TK/ukxdHxAu7seJmjvseP+Z78X/qPDXW0jHfiRK/aqRt8usSEfOB+R1Yf1tJujMipnc7H73E+6Smhsd9Lx/zvfg/dZ46pxM3d1cAexbG9wAe7sB6zHqJj3vrG50I/D8BpkraW9I2wLHAwg6sx6yX+Li3vtH2qp6IWC/pFOB7wJbApRFxf7vXM4Z68tK8y7xPqoyD474X/6fOU4e0/eaumZn1NnfZYGZWMg78ZmYlU+rAL2lPSbdIWiLpfkmn5vSJkm6WtDT/3SWnS9JF+ZH8eyS9urtb0DmStpT0X5K+ncf3lnRH3idfzzcwkbRtHl+Wpw90M982MknLJd0r6W5Jd9aYPqbHuKR9c14qwzpJp1XNMyhpbWGeszqQj0slrZZ0XyGtZhyo8d3ZeZ6lkma3O2+dUOrAD6wH5kXEy4AZwMmS9gPOABZFxFRgUR4HOAyYmoe5wBfHPstj5lRgSWH808Bn8z55Ajgpp58EPBER+wCfzfNZb3tzRLyyTnv0MT3GI+KBnJdXAq8Bnga+WWPWH1bmi4h/7EBWLgMOrUqrFwc2kDQROBt4Henp7bPr/UD0klIH/ohYGRF35c9PkgLd7qRH7S/Ps10OHJk/zwKuiOR2YIKkKWOc7Y6TtAdwOPDlPC7gLcA1eZbqfVLZV9cAB+X5rT918xg/CPhlRPxmjNa3QUTcCjxelVwvDhS9Dbg5Ih6PiCeAm9n0B6TnlDrwF+UqilcBdwCTI2IlpB8HYNc82+7AQ4Wvrchp483ngI8Bz+bxFwBrImJ9Hi9u94Z9kqevzfNbbwrgJkmLcxcS1bp5jB8LXFVn2usl/VTSdyXtP0b5qRcHivoyJjjwA5J2BK4FTouIdSPNWiNtXLWHlXQEsDoiFheTa8waTUyz3nNgRLyaVKVzsqQ3Vk3vyv8z3zN6B/CNGpPvIvVJ8+fA54HrO52fUejL47/0gV/S1qSgvyAirsvJqyqXt/nv6pxehsfyDwTeIWk58DVSFc/nSJf8lQf+itu9YZ/k6Tuz6SWz9YiIeDj/XU2qSz+gapZuHeOHAXdFxKrqCRGxLiKG8+cbga0lTRqDPNWLA0V9GRNKHfhzXfQlwJKIuKAwaSEwOwe/B4G9JQ0DJwDnSZojKYDtK5eCeXkrJA0WxqdK+pqk3+XWCkslfT7XoVdaK6wozD8k6feS9iykHZzzURlfLukZScOF4Qt52jaSzs/5GJb0a0mfrfruwfnz/VXLGJb0B+D0iNgDmEO6tN2GVNc5ARiW9HpgNnBDcV/lz0cDPwg/FdiTJO0gaafKZ+CtwH1Vsy0ETsite2YAa4vHeAe9mzrVPJJ2q9w3knQAKW49NgZ5Kh7bxWO+6HvAWyXtkm/qvjWn9baIKO0AvIF0WXYPcHceZpLqqBcBfyRdZk7M84v0so3VpBZBa4DnF5a3AhjMn/chlXwvAPbIabsCpwHH5vFBYEXh+0OkA3p+Ie1gYHlhfDlwcJ3tORv4D+BFOa8DwAlNfndH0s3tcwt5+x3w7Tz+EuDHwDLS5fi2OX27PL4sT39Jt/+vvTTkff4MMFwY3lP8v1f9/9+XP5+Tj7/i99YU5g1gnzrrnEIq0KwEngR+DpwL7A/8NA+PkLoXfiYfp/8X2LZwjK/L65hdWO4+KWRsGN8fuInUymsNsBiYmafNAW6rsz8Ozp8vy+s4Oh/3O+f0z+X0y/P4V/L4n0jn3S+AI4DjCvvmGdI9qQ37q9YxTyqRL8jreyofs0eQfnRW5n0epHN5EikOLAV+DXw1L2M68OXCMt+bj/9lwPvy/25pXv5y4FJgoDD/EXm9T+V8LCDHiMK+C+CCqn13ZE6/LI8P5PHKNq8C/hXYuuFx2e0To5eH6oOm6h9zG/At4OxCejHwXwl8q8HyB9k08J9NOln3yWmjCfzfJt2nGNX25GlfI53EW9TKm4f2HUP19i2bBv4rR1huzcAPTMzr/Gol2JCqIi4EXpHHP58D0+tJ/XXtnwPRDYXlXJaD0k2FtOrA/yvgo6Srwm1I1YRvyNPm0FzgfwC4tjB9K+C3pCA6p3pZpNL+B0nNPic2sU+L66vsm38HdgO2J11prAOOrtq3jwHvKaR9khxwG/y/F5IKi6/N27IzcDJwUp5+dF7fcXn9u5F+GJYDuxS2d1neD1sVln1d3l+X5fGBnNet8viuwH8xQgyoDKWu6mmDfwA+lNvyVjuYdO9gtH4LfIl04o/W7cCHJX1A0rRmm1VK+nvSSfueiHi20fzW0z5MKjj8TUQsB4iIhyLi1Ii4R9JU4APAcRHxnxGxPlJnckcBh0p6S2FZlwOvkPSm6pXkOva9gS9FxH/n4UcRcdso8/st4MBC2/dDSVfgj9SaOR+fl5KC5ktGua4PkUrGJ0XEIxHxTERcBXwKOL/qfPkMcG7hvlZDuRr1EGBWRPwk79u1EXFxRFySl38+8MmIWJDX/wjpKmE456/iEeBeUnPRyvMCf8EIPb5Gum9zM7Bfo7w68Dd2vaQ1heFvKxMi4m5SKfn0Gt+bROHglXRK/v6wpC81WOc/A28fodlavTz9M+kBquOAO4HfNnqSMNfj/hNwTERUv1noRVXrWZPrhq13HQxcN8IP+EGkkvGPi4kR8RCp4HBIIflp0rHxqRrLeYxUKr1S0pGSJreY39+TgtmxefwE4Ip6M+dAXAmUS0e5rkNIVxfV++ZqYC/gzwpp15FK5nNGsfyDgR/nfVnLvnk9G7Vcyvm5lo33PaT9cEL+fCzpHsMf6q1c0otIPxS3N8qoA39jR0bEhMJQHbTPAt4vabeq9MdIda0ARMQXImICqf5y65FWGBG/A74A1HtCsWaeIuJPuXRxIOlm7KeASyW9rNZCcqntG8DHIz2sU+3hqvVMiIinRsq71VT8oR5NU8R3Vf3o3tLEd15AqquuZ9II01fm6UX/B9hL0mHFxEh1C28mVVGcD6yUdGu+ohitK0g3lHcG3kTt5pozJK0hFabeDbwzItaOcj31tn1lYXpFkK7oz5K0bZPLb2bfU2eeWvv+m8Bg3i8j/SA+mvfNb0n3Da6pM98GDvybKSJ+TiodfKJq0iLgrzZj0f9COrFe02K+nomIi0k33ja59JO0Bake+EcR8fnNyKc1VvyhPpJ0g7LWj//WpJuLFVdX/ei+uYl1bVTgqOHREaZPoep9shHxB+B/5UFV01ZExCkR8VLgxaSgUwlOzW4juXrohcCZpMYEz9T43u15H0yKiBkR8f0RtrGeets+pTC9mK8bSa36aj3oVksz+54689Ta988A3yHtl0kR8aM6y52UC5XPA35EulE/Igf+9jgXOJFUyq44B/hLSRdI2h02lLBrlr6rRcQaUknqY81mQtJpuYno9pK2ytU8O5Fu+FQ7h3TT733NLt/a5kFgktKDg8CGpsUvBja3u4LvA+/MP+y1/ADYMzeL3CA3IZ5BKrBU+3fSTcp31ltprt64GHh5TnqQdKWw4cdC0vNINyBrbeOVwDxGqOZpg+8DR9XYN+8iPX37ixrfORP4n6Sg2szyD6g0167hAVIDkGOKiTk/R1F7319B2i9fabTy/ENxGekp5xGfc3Dgb+xbVW3dN+lAKiJ+TfrH7FBI+wXpRNoD+KmkJ0m/xg+TLiGbcSGpCVuzeXqG9GNRaap3MnBURPyqxjLOJN0ce6RGe/698jwvqjHtqCbzbnVExIOkrkE+LWnHXJXwUVIpuWH9bME2krYrDFuSmg8/H7hc0osBJO2eCyCvyMflvwELJM1Q6oV1f1Id8/drlaQjdcVxDoV7Wbnd+rmS9pG0RQ407y3k/w5S/f0ZOW87AOeR7j3VCvwXkeq4bx3F9o/WZ0n75hKlZwO2k/RuUmD/aK6+2khEDJFusjbsdTPvu5uBb0p6TS587STp7yS9Ny//I8CZkt6TC2i7kfrEen7OX7X/IO2Xhlfl+Tg6nnT+j/ycQ6NmPx48eGh9oH6T4D1J91cqP9LfA/YrTD+HTdvxDwO75ulRY6g0BX0RqeXLIzzXjv9s4Hl5+hakIL6MVFh4iNSKZbvC+i8jtT6h8J372FC9zw6kVj/Lc74eIbWF373wnf3ydj1KamN+DbBnvXVU7Z/bqNGcc4T9PEiD5px5fK+cz8dJVVM/IbXCKX5no6aypJ43N7Sfb5CPbUg1AMvy8n9DCux7FeaZldf7VM7HVVX7pe72UmhWyqbt+NeQfihe2yiffvWimVnJuKrHzKxkHPjNzJok6bga972GJd3f7byNhqt6zMxKpunHkTtp0qRJMTAw0O1s8NRTT7HDDv3zYGq/5Rc6m+fFixc/GhEv7MjC26xXjvl6+vHYapd+2vZWj/meCPwDAwPceecm730ec0NDQwwODnY7G03rt/xCZ/MsqS2v7JO0HalZ4bakc+SaiDhb0t6kzuwmkjriOj4i/js3o7uC9LDdY8BfR+4np55eOebr6cdjq136adtbPeZdx2+2qT8Ab4n0xqdXkjovm4FfOG/jhAO/WZVIhvPo1nkI/MJ5Gyd6oqrHrNfkp2AXk/qgvxj4JU2+cF5S5YXzj1Ytcy6535fJkyczNDTU4a1o3fDwcE/nr5PKsO3jMvAPnPGdUX9n+XmHdyAn1q8i4k/AKyVNIPWSWKuPpUqTuKZeuB0R84H5ANOnT49u1yOPdJ7Mm/Ynzr+tdkes4/1c6ac6/la5qsdsBJE6yxsi9bvkF87buODAb1ZF0gtzSR9J25NesLEEuIX06jzwC+etj43Lqh6zzTSF1LvllqTC0dUR8W1JPwO+JumTpK6uL8nzXwJ8RdIyUkn/2FoLNesVDvxmVSLiHuBVNdJ/BRxQI/33VPWxbtbLHPg3Qys3kWH83xwzs97mOn4zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OSceA3MysZB34zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OSaRj4Je0p6RZJSyTdL+nUnD5R0s2Slua/u+R0SbpI0jJJ90h6dac3wszMmtdMJ23rgXkRcZeknYDFkm4G5gCLIuI8SWcAZwCnA4cBU/PwOuCL+a+ZjQN+w13/a1jij4iVEXFX/vwk6YUUu7PxC6arXzx9RX5h9e2ktxZNaXvOzcysJaPqllnSAKmf8juAyRGxEtKPg6Rd82wbXjydVV5KvbJqWR178fS8aesbz1RlaGho1C9ZbmU9lXW1Qz++FLof82w23jQd+CXtCFwLnBYR66Ra75dOs9ZIG9MXT89p5VL0uMFRv2S5lfVU1tUO/fhS6H7Mcz9o9d0QVk5NteqRtDUp6C+IiOty8qpKFU7+uzqnb3jxdFZ8KbWZmXVZM616RHqn6JKIuKAwqfiC6eoXT5+QW/fMANZWqoTMzKz7mqnqORA4HrhX0t057RPAecDVkk4CHuS5d47eCMwElgFPAye2NcdmZrZZGgb+iLiN2vX2AAfVmD+AkzczX2Zm1iF+ctesih9atPHOgd9sU5WHFl8GzABOlrQf6SHFRRExFViUx2Hjhxbnkh5aNOtZDvxmVfzQoo13o3qAy6xsxvNDiyOZvH17l9lPD+2V4SFDB36zOsb7Q4sjmTdtPeff277w0K6HFsdCGR4ydFWPWQ1+aNHGMwd+syp+aNHGO1f1mG3KDy3auObAb1bFDy3aeOeqHjOzknHgNzMrGVf1ZANnfId509a3vVmcmVmvcYnfzKxkHPjNzErGgd/MrGQc+M3MSsaB38ysZBz4zcwipsvqAAAHB0lEQVRKxoHfzKxk3I6/TwzUeL6g0XMHy887vJNZMrM+5RK/mVnJOPCbmZWMA7+ZWck48JuZlUzP39ytdVPTzMxa5xK/mVnJNAz8ki6VtFrSfYW0iZJulrQ0/90lp0vSRZKWSbpH0qs7mXkzMxu9Zkr8lwGHVqWdASyKiKnAojwOcBgwNQ9zgS+2J5tmZtYuDQN/RNwKPF6VPAu4PH++HDiykH5FJLcDEyRNaVdmzcxs87V6c3dyRKwEiIiVknbN6bsDDxXmW5HTVlYvQNJc0lUBkydPZmhoqOaK5k1b32IWR2/y9mOzvnrbOpJa+WqU31bW02nDw8M9mS/rrFYaafjJ885pd6se1UiLWjNGxHxgPsD06dNjcHCw5gLH8lWI86at5/x7O9/Qaflxg6P+Tq390Ci/rayn04aGhqj3v+4lki4FjgBWR8TLc9pE4OvAALAceFdEPCFJwIXATOBpYE5E3NWNfJs1o9VWPasqVTj57+qcvgLYszDfHsDDrWfPrGsuw/e2bJxqNfAvBGbnz7OBGwrpJ+TWPTOAtZUqIbN+4ntbNp41rNeQdBUwCEyStAI4GzgPuFrSScCDwDF59htJl7vLSJe8J3Ygz2bdsln3tpq9r9WKdt+bGqv7XSPp1r2gMtyHahj4I+LddSYdVGPeAE7e3EyZ9Zmm7m01e1+rFe2+FzZW97tG0q17VP1yH2pz9HyXDWY9ZJWkKbm073tbHeaWQJ3jLhvMmud7WzYuuMRvVoPvbdl45sBvVoPvbdl45qoeM7OScYm/C/yOATPrJpf4zcxKxoHfzKxkHPjNzErGgd/MrGR8c3cc85OPZlaLS/xmZiXjEr9tpNWmpr5SMOsfLvGbmZWMA7+ZWck48JuZlYwDv5lZyTjwm5mVjFv1mNm44VZpzXGJ38ysZFziN+sx7rbbOs0lfjOzknHgNzMrGVf1mFnpFavX5k1bz5wmqtv6+YawS/xmZiXjEr+ZWQv6udvzjpT4JR0q6QFJyySd0Yl1mPUaH/fWL9pe4pe0JXAxcAiwAviJpIUR8bN2r8usV/i4t2b0ylVCJ6p6DgCWRcSvACR9DZgF+ASwjfTKSdAmPu6tb3Qi8O8OPFQYXwG8rnomSXOBuXl0WNIDHcjLqPw9TAIe7XY+mtVL+dWnm551s/LcYD0vbnW5bdDwuO/FY76eXjq2xlqvbXsnjvlOBH7VSItNEiLmA/M7sP6WSbozIqZ3Ox/N6rf8Qn/muUkNj/tePObrGcf/p4bKsO2duLm7AtizML4H8HAH1mPWS3zcW9/oROD/CTBV0t6StgGOBRZ2YD1mvcTHvfWNtlf1RMR6SacA3wO2BC6NiPvbvZ4O6YvL8IJ+yy/0Z54b6vPjvpZx+X9q0rjfdkVsUv1uZmbjmLtsMDMrGQd+M7OSKVXgl7SnpFskLZF0v6RTa8wzKGmtpLvzcFY38lqVp+WS7s35ubPGdEm6KHcVcI+kV3cjn4X87FvYf3dLWifptKp5em4/l5WkSyWtlnRfIW2ipJslLc1/d+lmHjuhXjwoxbaXqY5f0hRgSkTcJWknYDFwZPGxekmDwEci4oguZXMTkpYD0yOi5kMlkmYCHwRmkh4aujAiNnlorhtyVwa/BV4XEb8ppA/SY/u5rCS9ERgGroiIl+e0zwCPR8R5ud+hXSLi9G7ms93qxQNgDuN820tV4o+IlRFxV/78JLCE9MRlv5tFOmkjIm4HJuSDuhccBPyyGPStt0TErcDjVcmzgMvz58tJAXFcGSEejPttL1XgL5I0ALwKuKPG5NdL+qmk70raf0wzVlsAN0lanB/7r1aru4Be+UE7FriqzrRe28/2nMkRsRJSgAR27XJ+OqoqHoz7bS9lf/ySdgSuBU6LiHVVk+8CXhwRw7kK5Xpg6ljnscqBEfGwpF2BmyX9PJfSKprqJmOs5QeZ3gF8vMbkXtzPVkLV8UCqdTqNL6Ur8UvamvRPXhAR11VPj4h1ETGcP98IbC1p0hhnszpPD+e/q4FvknqCLOrV7gIOA+6KiFXVE3pxP9tGVlWqC/Pf1V3OT0fUiQfjfttLFfiVfsovAZZExAV15tktz4ekA0j76LGxy+Um+dkh33hC0g7AW4H7qmZbCJyQW/fMANZWLlW77N3Uqebptf1sm1gIzM6fZwM3dDEvHTFCPBj/216yVj1vAH4I3As8m5M/AewFEBH/lh+7fz+wHngG+HBE/L8uZBcASS8hlfIhVc19NSI+JenvYEOeBXwBOBR4GjgxIjZp9jmWJD2PdN/hJRGxNqcV89xT+7nMJF0FDJK6I14FnE2qeruadG48CBwTEdU3gPvaCPHgDsb7tpcp8JuZWcmqeszMzIHfzKx0HPjNzErGgd/MrGQc+M3MSsaB38ysZBz4zcxK5v8DhtoJDID9LSsAAAAASUVORK5CYII=\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": "markdown",
   "metadata": {},
   "source": [
    "Now, lets plot each of these features vs the Emission, to see how linear is their relation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "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": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 8,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "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": "light"
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "plot __CYLINDER__ vs the Emission, to see how linear is their relation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "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": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel('Cylinders')\n",
    "plt.ylabel('Emission')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
    "\n",
    "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Simple Regression Model\n",
    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the independent x in the dataset, and the dependent y by the linear approximation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LideLaF00aGQk+mu/3ebNM6ODWhVNUtdp/zyfF9cx6ecdHk6uexYl2hs8StYnlctK4dz0OqRNQsu6CN9wQ/TYvlLb2Fh6oCkrbfWLXtT5/eHhzsEBijelyQKWp6OiqZs6qetTNAFcE+uQ1SlddERSN8cU3b+MpHZFO+OzdPM9Sb3I2UmtJibpStZaCYNQh6ymkW6aToo23xT9jDKah+I7p3ZZdx5p1MQ0eEptYgojkv6TmW03syvjrfdqyqBqwsiVXuuQNcFrYiL5/dby9iauM85IPiZtfea0i2taeRnNQ9u2RcGgdTJet8EB0ifSpZXL4MjbB/FV4PnA9UQT2uJNFqi0ETx5EsP1sw6d+ijSLtpx+fXXzw0ScQd1fO5Nm6K7GPfo8eab5ybOm5hIv/i+//3Fysuatdw6dPbw4ezg0Ol7XL9+7ucPD0flMuDytEMBd+TZr9+b+iDqMwh9EHnq2MsaBGkTxNrTa2d9L0XqUMfCOlnfoybKDR7KnElNNFdhQ559+7kpQNSrCQu8dJoRXMaFq9P586yzUMXFsozvvchM6qzvsZs1MKReZQeIJ4HngF+E508CP8tzbJWbAkR9mnAHEdej6AU874Wr27+cm36xLPq7y/oe01bRS0svIvUrNUA0dVOAqE8TmhWyLnRLlybXcenSfOfP+hl37py7/nTalra8Z3yesvIilfFzFd0/7TtYvHjmHP3+GaWz0gME8Dbgz8P21rzHVbkpQNSnimaFoheRqps+Ol3w4/q2Z0UdGsoOGu39JEUzqy5ZMnv/JUvy/Tyxot9LViDO8z21z70YHlaQqFPZTUyfIEq//Tthuw74RJ5jq9wUIOpT9h1EN01WWRe6rAtXlqzzp30HK1bMBLq0SWnx91S0eaY9OHQTJLr53XUK3lnfc693clK+sgPEXcBQy+th4K48x1a5KUDUp+w+iLSLVqemmawLXa8BIuv4PH+JZ52jaB17/Znck5vGFi/u/neXlbK8jDpLufIGiCK5mJa3PH9+geNkHkrKQ9TLQvRpk9uOHIkuJXv3RnMOWsffX3QRjI7O3n90NCrvhzzzMJq62lr0d1766yLe975i5TJA8kQR4F3AXuAqolXgHgTOyXNslZvuIOaPvCOC2ptBemn6yJJ1h5DnLqqOO4isYbBVDDDo9Jl5FkWS/qKCTupVRB3VZwH/Iu9xVW4KEPPHzp3pF5L2Le8cgDxJ6ToFmDyT0rI61rMuxsuWJb+/bFnyz5TVB5Gnzv2et1DH5D7prJQAAfxKeDw1acvzAVVuChDzR7drL3e60GRdmPK0xfc6KS3rLqObOQSdRjHl+Wu9jnkLTZhUKTPKChDbw+NNCduNeT6gyk0Bol5ljm0vuuZy2h1Be/1GRmbvOzLS28U5j/bvZfPm6prB2uU5X9rPbaZ5CgtF6U1MRTfgaOBW4E7gXuDjofxE4BbgfuCvgMWh/Kjwend4fzzrMxQg6lP2KKZug0Oni2nVo5ySFP1e6ggQeVKE1DErXvonb4DIm+77HWb2S+H5fzazr5jZKzMOewY4091fAZwCvMnMTgf+FLjY3U8GfgKcF/Y/D/iJu58EXBz2k4baunX2UpsQvd66tbvz9TKqJ+3YOlKSl/29FJUnfXiejLv9rLM0V95hrv/F3Z80s98A3kg0kumyTgeEQPVUeLkobA6cCXwplO8Azg7PzwqvCe9PmOVdgVf6LWmhnk7lWdJSb/dy7NKlnctXrEh+P608j7Tgs3dveUuzbtkSLRxkFj1u2TLzXnQzPldr+YYN+T6nn2t7SDPlDRDxciRvAS51968Ci7MOMrNhM7sDeJRo9vU/AT9193jtq33A8eH58cDDAOH9J4Ae/qtKlcoe379+fXQBzbJs2eyFbiYmYNeu5Ivvz3+efI64/JJLYNGi2e8tWhSV59V+sU4LShBdpPfuhXPP7T5ItK+jfeRI9Lo1SGTZtSvffv1c20MaKk87FHAtcDnRBX45UX/BnXmODccvJ+rY/k1gd0v5CcDd4fm9wOqW9/4JWJFwrk3ANDC9Zs2a0tvmJJ88bd1lpJRO60Mwizpb20chFckRVLSO7dJGSbV3jHfqCC/aB9HLOtrxz5nnOy7aB6FkfIOFklNtjAK/DZwcXq8C3pDn2JZzfAz4I+AxYCSUvQb4Rnj+DeA14flI2M86nVOd1PXJk+m0rM7aoluZndCdhpSmXazji2TWxTjP91jke+r1e+x2FFM3CQelXnkDRN4mplXA19z9fjM7A3gH0QilVGEd6+Xh+RLgdcB94U7i7WG3jUTLmQJcE14T3r8x/CDSQFnt+3V21vbSdt7aZGQGTz89+/2nn55J75G2DrQ77NkDzz2X/Xlp/QF5+wnKZBbVec+eYilTLrgAnn12dtmzz0blMtjyBogvA0fM7CTgCqKhqv8r45hVwE1mdhfwHeA6d78W+DDwQTPbTdTHcEXY/wpgRSj/IPCRQj/JPNNpDeAmnO973+tcXscIoli3beft7ftp4qBRRj/M1VcXK8/Sy2iwPAEtyeOPFyuXAZLnNgO4PTx+CPi98Py7eY6tcpuvTUxlzzGoYvW3fjadFNlaf66iKSWKTNZzz5dCopcmoW6+916+u7QJh1mK/gxSP0rug7iFKGHfPcCJoeyePMdWuc3XAFF2MrUqkrNlXRSKBqW8K7OlbWXMUi7yebGsFBJZs7XLDhBpv+s8ndPdpr/QkqODJ2+AyNvEdC5Rh/JF7v6gmZ0I7CzvPkZald08U0VzT9aErKLpwM87L7k8r27aztvlGWYLsGTJzPP162H16uhnXL06et0qayht2memlW/e3Lk8LQX6mWfOHh68du3s15s3w7ZtyefOcsklsLht0PvixcWGC0tD5YkiTd3m6x1E2X+RVfEXXtnNCkWGueb9zKI/d9rKZ61ba9K7vHdJvWaMbZd115KUC6rsJsZ2GuY6WCgpWd8Xw+PdRKvKxdvdaEW5yizEAJF3fH6Rz5yYSN53YqK3OsQX5DxLjmZdLPuRCruKJkYZbHkDhEX7JjOzVe6+38zGUu4+ukysUI5169b59PR0nVWoxNBQ9F+4XTwMse7zxcem6fBPKtVxx/U26iXpM0dGkkckDQ/D4cNzy8fH86UKiY9P+17bjY6mN68VrSNEI9C2bo2aCNesiZqVOjWtVfH7l8FmZre5+7qs/Tq2urr7/vC4NwSDnwBPtmxSgTxLWdZ5vkGRNlw1rTyp/b7T8Xm/v07zP4rWcWoqyj21d2900U9airVdP37/ZQ+jlobIc5sBvA/4EbCHaLnRB4EH8hxb5TZfm5jKXlS+jLbydmU3MfXSvNS6rV07c848K8olfVdZs6Dj45O+17St6NDatDp201xUxTDnfp5fykfJw1zvB47Ls28/t/kcIMpOXZB18S/6md0EiG7Xj+42SPTaAZwWJDotOZrW35N2AS9ax26XC62yE1l9HIOn7ADxdWA0z7793OZrgKjjP1zRjuyiASLrr8wyA0RrHYosdZm17GmepTJ37px7VzA8nN1RnbeOTbwY93uNa+ld3gDRsZM6FhYH+izRhLlnWpqn/mNpbV1dUCd1eYp2OufZv7UzdWgouV19bCyav1D2yh85/lnPUcb3HqfraNfLPIN+nr8baZ378e9WmqeUTuoWlwM3At8GbmvZpAJpHaV5OlCbor0zNa3TtdsFhqqQFlSKBJvLLy9WXlTaWg5513ioQtrkvIsuqqc+Up6RnPsddvcPVloT+WftGUSzysuwYkXyMNO01dWGhpL/qo5nACdlc03SS3K5NGvXln/OvNLuNMq686szCWKaeIhtkaG3Mhjy3kHcZGabzGyVmR0bb5XWbAGr+iKTpGi6hKw65r1gZWVOLWrtWrj33u6OXbasWHkdmjpkeXJyJsV5rylPpDnyBoh/B1wI/CMzzUvzr/F/Hmkfl75lS+dx6pOTcOWVs3MnXXll9//R816weln/uVXcNdoeHIqMz7/ssmjiWquRkai8KdScI32Vpye7qdt8HcVUdIRQuzzj86tO9501Iqh9lFSvo5aShm92Mz6/1+Ggvf7u8mhi3qMm1knSUVIupg+1PH9H23t/kucDqtwUIJLlTXxXZbrvtGGzafv3GiDirXVCYR1DQvsRIJpGE+UGT94AkZWL6XZ3P7X9edLrOszXYa5peYlWrIDHHss+Pu+Q0SpzMRUZtmpWbKRQlvh7qmO4cK+/u0GkYa6Dp6xhrpbyPOl1ewVOMLObzOw+M7vXzC4I5X9sZj80szvCtqHlmAvNbLeZ/cDM3phV+fmqX/n16+7YjJUZHGDmAl1Hh+5CXBuhiSOrpBxZAcJTnie9bncY+AN3fylwOnC+mcUDEC9291PCtgsgvHcO8DLgTcA2M6tgEGTzld1hnCSpY7PMhGtlT3zrRh0duv343TVNU0dWSe+yAsQrzOxnZvYk8Gvhefz6Vzsd6O773f328PxJ4D7g+A6HnAV8wd2fcfcHgd3Aq3L/JJJL2upu3WQJ7aTsu4JuFF3VrszPXUhDPjWyah7L01HR6waMAw8BzwP+mCgr7F3AlcAxYZ//Cby75ZgrgLd3Ou987aTeuXNufhuz/J1+3XSUFu3Q7baTemgo+lnSspiWtUl/aRTTYKHkNam7ZmbLgC8DH3D3nwGXAi8GTgH2A38R75pw+Jy/Q8OEvWkzmz5w4EBFta7X7/zO3L/A3aPyqvSrHfmYY6K/rF/yknLPK/VaaHdNC0WlAcLMFhEFhyl3/wqAu//I3Y+4+3PAp5lpRtoHnNBy+GrgkfZzuvt2d1/n7utWrlxZZfVrc+hQsfJ2aQvep5VD+e3IP/5x5/Lvfa+784pI/1QWIMzMiJqJ7nP3v2wpX9Wy228B94Tn1wDnmNlRZnYicDJwa1X1m8+6SdVRdjuyOi5FBl+VdxDrgfcAZ7YNaf3vZna3md0FvBb4fQB3vxf4IvA9ovUnznf3kjP1iFmUPmLLltnlZXfoquNSZPDlWg+iqebrRLmiazO0S5us1a6XNQSWLIFf/GJu+dFHz2SdbV0Poj3DZ5XDYOfzpDSRMpS9HoQMkHe+M99+27fPfr1lS3R3kXaX0eqXf7lYeb8sWjS/J6WJ9JMCRAXKnHDWjauvzrdfa6rteKWyuOzIkeh1WpBI62SOy8ueV9HJsmUzzWKf/axG0IiURU1MJYsvjK2L5YyOFmvP79T8Ypa9IEs3uZhGRpLXZhgehsOHi32Ge3Z+njKbmNLqKCLJ1MRUk6SV1A4ejMrLUOZf40uXzjxPW7in2wV9+pmfp+xFh0QkogBRsn5dGMsIOk89VU5dkvRzmGsVy5aKiAJE6fp5YWxytsysYa5jY+V91qZN5Z1LRGYoQJRsw4Zi5b1o8qSzrHkVJ53U/bnjO4bh4d6G6opIZ+qkLlkZC8bk6cDt1PFdpAM4/vUXXVyn17kaaZ3iWZYurbZpTGQhUCd1TdImqOWZuJZHVWmr3//+YuW96iY4DA3B5ZeXXxcRSTZSdwWkmKqWyoybabZvjy7ew8NR235VzTfDw/mDRJ6hvSJSPt1B1KTIrOWilizp7rht26L5BO7RY5Vt+0U6lpVCWqQeuoMomVl6W34snrUci2ctQzkX5TgXUpXy/JydtN+xpOmUolxEqqX/fl3olEojrYO2tbw9B1JWeb8USRHy0pcWK0/SescyMZG8z2tfm/98IlIu3UEU1J5KI57VDPmbQMqetVyGoj/XD36QfJ608iy7dxcrF5HqaZhrQb3kGIq/6qy8R70OIe1mmGvWz1XkM7r5J1X2+UQknYa5ViTpItqpPMkZZxQrr8qLXjTzvGiKkLT0Ft2mvSj7fCLSOwWIgsq4kDWlOeWRlhW/i6YISRuF1G3aiyY2u4ksdFWuSX2Cmd1kZveZ2b1mdkEoP9bMrjOz+8PjMaHczOxTZrbbzO4ys1OrqlsvyriQ9TPTaV5Flwjdti1Kc1FW2ou03Exl5mwSkWKqvIM4DPyBu78UOB0438zWAh8BbnD3k4EbwmuANwMnh20TcOncU9avjAvZsccWKy+qtekor27WpC5z3kQ/c1iJSD6VBQh33+/ut4fnTwL3AccDZwE7wm47gLPD87OAqz3ybWC5ma2qqn7dKuNC9uSTxcqLeuKJ7o7yQ/3RAAAOlElEQVSbnIw6pOuYmLZrV7FyEaleX/ogzGwceCVwC/BCd98PURABXhB2Ox54uOWwfaGsUdIuWNu355/UdehQsfKifv7zcs7TT01sdhNZ6CoPEGa2DPgy8AF3/1mnXRPK5gxwNLNNZjZtZtMHDhwoq5q5pV2wjhzRcMxe9HMdDRHJp9IAYWaLiILDlLt/JRT/KG46Co+PhvJ9wAkth68GWsbZRNx9u7uvc/d1K1eurK7yKXTBqkbRTnIRqV6Vo5gMuAK4z93/suWta4CN4flG4Kst5e8No5lOB56Im6KaRJ2m1ZichI0bZ4+K2rhRCfpE6lTlHcR64D3AmWZ2R9g2AJ8AXm9m9wOvD68BdgEPALuBTwMl5jctz+c+V3cNsi1dmm+/IjOuqzY1BZddNjNc+MiR6HWnfFAiUi2l2iiol4tq/FVnpZXoNe3E1BS85z3Z+zZpdbajj4ZnnplbftRR8Itf9L8+IvOZUm0scCM50jA2abRTUnDoVC4i1VOAmIe2boVnn627FiIy6BQg5qG8iQNXrKi2HiIy2BQg5qE8iQMXL4ZLLqm+LnmlLRiUVi4i1VOAmIeyEgcOD8N55zVrCOn1188NBhMTUbmI1EMBYh7KShx45Ah85jPNG0J67rmzkwWee27dNRJZ2BQg5qGkWcntnn0WLrigP/XJI17ydO/eaHhuvORp04KYyEKiADEPtafuTvP44/2rU5atW2fWw44dPBiVi0g9FCDmqdbU3YOgjKVcRaRcChA1SEuFEZeXPaJn2bJi5XXQmtQizaMA0SfLl888T2v2icvLXrP66aeLlddBa1KLNI8CRJ88//kzz9PyH8XlZTe3DMLFV2tSizSPAkSfaGW0zrQehEjzKEAkmJqC8fFoCdHx8XKGWh57bO/nmM/aR16NjUWvmzSZT2ShyZHzc2GJx+PHQy7j8fgQXayWLWtOiuy8JibghhuSy5tkclIBQaRJdAfRJms8/mWXdbcmxI9/3HvduqU0FiLSDd1BtEnrK2gtX7QIDh0qdt6617JWMBCRoqpck/pKM3vUzO5pKftjM/th2xKk8XsXmtluM/uBmb2xqnpB5z6GtAt5XL51a/HgsGjR7M7WrHkJixcnv59WLiJShSqbmK4C3pRQfrG7nxK2XQBmthY4B3hZOGabmVUyRSor50/WaJpuRiO1N0lddtncFd9GRqJygCuvnHuMWVQuItIvlQUId/97IG/L+1nAF9z9GXd/ENgNvKqKemX1MWSNpkm7wxgejvZPmvl76NDsnEKTk3DVVbM/46qrZj5jchI+97nZ73/uc+rAFZH+qqOT+j+Y2V2hCeqYUHY88HDLPvtCWeny9DF0knaHsWNHlPcoLfdR+/lbcyXt2TP34p/1vohI1fodIC4FXgycAuwH/iKUJ40L8qQTmNkmM5s2s+kDBw4UrkBaGuy4PKsJqts7jKKd1L3OxahiLoeILDDuXtkGjAP3ZL0HXAhc2PLeN4DXZJ3/tNNO86KGhtyjS//sbWgoen9sLPn9sbF859+50310dPaxo6NReV69nmPnTvfFi2cfv3hxsTqIyPwFTHuOa3hf7yDMbFXLy98C4hFO1wDnmNlRZnYicDJwaxV1SGsCist7bYIqY0Zwr2sjXHDB3JFWhw41a4EgEWm+yuZBmNnngTOA48xsH/Ax4AwzO4Wo+WgP8D4Ad7/XzL4IfA84DJzv7pWkkhseTk5SF3cur1mTnBSvSBNRrzOCew1SaQsBPf541OS0Zk3Ul6J+DRHppMpRTO9y91XuvsjdV7v7Fe7+Hnf/VXf/NXd/m7vvb9n/Ind/sbu/xN3/pqp6xWkz0so3bEh+P60cym/vT8vbVEY+Jy3nKSJ5LbhUG9u2webNM3cMw8PR623bote7diUfl1bexLWUV6zI3kfLeYpIFov6KwbTunXrfHp6utRzDg1FF/p2Zsn9F+PjyU1SY2PR8NR+1KHd1BScey48+2zn/fKeT0TmFzO7zd3XZe234O4gshQdptprf0EZdWg3OQmf/exMR3nasp1154cSkWZTgGhTdOGabi/mnfotylg8p3WiXVq/S6d+FRERBYg2RYepdnMx73UyXlFF+1VEREABIlGRNBfdXMx7nedQVBXNYCIy/ylAdKG9eQiK5U3KumCXPTKqrPQfIrKwKEAUVMbFO8+aE2XeYZTRpyEiC48CREFlXLy7XXOi2yahsvs0RGRhUIAoqIyLd78ywrZ/ptKHi0gRChAFlXXx7nTBVpOQiDSBAkRB/bh4q0lIRJqgsmyu81V8kd66NWpWqiozaq8ZYUVEeqU7iC6U0Z6flQFWK8KJSN0UIEpQ9GKeNVS2iRliRWThUTbXHsUX89ahr6OjnfsMsjLAVpEhVkQkljebqwJEj7q5mGel8+413beISCe1p/s2syvN7FEzu6el7Fgzu87M7g+Px4RyM7NPmdluM7vLzE6tql5l62ZeRNZQWaXGEJEmqLIP4irgTW1lHwFucPeTgRvCa4A3AyeHbRNwaYX1KlU3F/OsobKaByEiTVDlmtR/D/y4rfgsYEd4vgM4u6X8ao98G1huZquqqluZurmYZ81z0DwIEWmCfs+DeKG77wdw9/1m9oJQfjzwcMt++0LZ/j7Xr7Bu50VkzXPQPAgRqVtTJspZQlli77mZbSJqhmJNQxrldTEXkfmo3/MgfhQ3HYXHR0P5PuCElv1WA48kncDdt7v7Ondft3LlykorKyKykPU7QFwDbAzPNwJfbSl/bxjNdDrwRNwUJSIi9aisicnMPg+cARxnZvuAjwGfAL5oZucBDwHvCLvvAjYAu4GDwLlV1UtERPKpLEC4+7tS3ppI2NeB86uqi4iIFKdcTCIikmigU22Y2QEgIdFFbscBj5VUnaqojuVQHcuhOpaj7jqOuXvmKJ+BDhC9MrPpPPlI6qQ6lkN1LIfqWI5BqCOoiUlERFIoQIiISKKFHiC2112BHFTHcqiO5VAdyzEIdVzYfRAiIpJuod9BiIhIigUZIJIWM2oSMzvBzG4ys/vM7F4zu6DuOrUzs6PN7FYzuzPU8eN11ymNmQ2b2XfN7Nq665LGzPaY2d1mdoeZ1btMYgozW25mXzKz74d/m6+pu06tzOwl4fuLt5+Z2Qfqrlc7M/v98H/mHjP7vJkdXXed0izIJiYz+9fAU0RrULy87vq0C4kMV7n77Wb2S8BtwNnu/r2aq/bPzMyApe7+lJktAr4FXBDW82gUM/sgsA54nru/te76JDGzPcA6d2/s+H0z2wH8X3f/jJktBkbd/ad11yuJmQ0DPwRe7e69zJUqlZkdT/R/Za27P21mXwR2uftV9dYs2YK8g0hZzKgx3H2/u98enj8J3Ee0PkZjhMWdngovF4WtcX9tmNlq4C3AZ+quyyAzs+cB/xq4AsDdDzU1OAQTwD81KTi0GAGWmNkIMEpK5uomWJABYpCY2TjwSuCWemsyV2i6uYMobft17t64OgKfBD4EPFd3RTI48LdmdltY86Rpfhk4AHw2NNd9xsyW1l2pDs4BPl93Jdq5+w+BPydKVrqfKHP139Zbq3QKEA1mZsuALwMfcPef1V2fdu5+xN1PIVq/41Vm1qjmOjN7K/Cou99Wd11yWO/upxKtz35+aAZtkhHgVOBSd38l8HNm1pRvlND89Tbgf9ddl3ZmdgzREssnAi8ClprZu+utVToFiIYK7fpfBqbc/St116eT0NTwTeBNNVel3XrgbaF9/wvAmWa2s94qJXP3R8Ljo8BfA6+qt0Zz7AP2tdwlfokoYDTRm4Hb3f1HdVckweuAB939gLs/C3wF+Fc11ymVAkQDhQ7gK4D73P0v665PEjNbaWbLw/MlRP/wv19vrWZz9wvdfbW7jxM1Odzo7o37a83MlobBCIRmmzcAjRph5+7/D3jYzF4SiiaAxgyaaPMuGti8FDwEnG5mo+H/+QRRH2MjLcgAERYzuhl4iZntCwsYNcl64D1Ef/HGQ/Y21F2pNquAm8zsLuA7RH0QjR1G2nAvBL5lZncCtwJfc/ev11ynJL8HTIXf+SnAn9RcnznMbBR4PdFf5o0T7sC+BNwO3E10DW7srOoFOcxVRESyLcg7CBERyaYAISIiiRQgREQkkQKEiIgkUoAQEZFEChCyYJjZkbZsn13PBDazfyyzbm3nXmdmn6rq/CJ5aZirLBhm9pS7L6u7HiKDQncQsuCFtRg+bma3hzUZfiWUrzSz60L55Wa218yOC+89FR7PMLNvtqyTMBVmyGJmp5nZ34UEfN8IadzbP/sdYV2AO83s71vOeW14vqvljucJM9sYkiT+mZl9x8zuMrP39eu7koVFAUIWkiVtTUz/tuW9x0KyvEuBPwxlHyNKz3EqUX6kNSnnfSXwAWAtUdbT9SGX1v8A3u7upwFXAhclHPtR4I3u/gqiBHOzuPuGkBDxPGAv8H/C8yfc/deBXwd+18xOzP81iOQzUncFRPro6XCxTRKnZrgN+O3w/DeA3wJw96+b2U9Sjr3V3fcBhPTn48BPgZcD14UbimGi9M7t/gG4Kiwck5geIty1fA54p7s/YWZvAH7NzN4ednk+cDLwYEr9RLqiACESeSY8HmHm/4UVPLb1eAPudfeOy3K6+/vN7NVEixrdYWazAlhYGe0LwH919ziBnwG/5+7fyFk/ka6oiUkk3beAdwKEv9qPKXDsD4CVFtZtNrNFZvay9p3M7MXufou7fxR4DDihbZdPAHe5+xdayr4BbA7NWJjZv2z44j0yoHQHIQvJktAEFPu6u3ca6vpx4POhr+LviJqInszzQe5+KDQBfcrMnk/0f+2TwL1tu/6ZmZ1MdFdwA3An8G9a3v9D4N6Wen+UaPnUceD20CF+ADg7T71EitAwV5EUZnYUcMTdD4c7gUs79GGIzDu6gxBJtwb4opkNAYeA3625PiJ9pTsIERFJpE5qERFJpAAhIiKJFCBERCSRAoSIiCRSgBARkUQKECIikuj/AzExtL7/6tgoAAAAAElFTkSuQmCC\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.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.39719721]]\n",
      "Intercept:  [125.10969579]\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": [
    "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can plot the fit line over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 13,
     "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": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\n",
    "we compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\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": 14,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.12\n",
      "Residual sum of squares (MSE): 867.73\n",
      "R2-score: 0.74\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) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Want to learn more?\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
    "\n",
    "### Thanks for completing this lesson!\n",
    "\n",
    "Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
    "\n",
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
   ]
  }
 ],
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