Created on Cognitive Class Labs

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": [
      "--2020-01-25 13:17:48--  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  --.-KB/s    in 0.06s   \n",
      "\n",
      "2020-01-25 13:17:48 (1.10 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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       "<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",
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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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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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  "
      ]
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     "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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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</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,
    "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 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,
    "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,
    "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": 10,
   "metadata": {
    "button": false,
    "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(\"Cylinder\")\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": 11,
   "metadata": {
    "button": false,
    "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": 12,
   "metadata": {
    "button": false,
    "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(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": 13,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.92364045]]\n",
      "Intercept:  [125.99299405]\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": 14,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": {
    "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": 15,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.37\n",
      "Residual sum of squares (MSE): 844.61\n",
      "R2-score: 0.71\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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