Week 2/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",
    "<h1><center>Simple Linear Regression</center></h1>\n",
    "\n",
    "\n",
    "<h4>About this Notebook</h4>\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": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#understanding_data\">Understanding the Data</a></li>\n",
    "        <li><a href=\"#reading_data\">Reading the data in</a></li>\n",
    "        <li><a href=\"#data_exploration\">Data Exploration</a></li>\n",
    "        <li><a href=\"#simple_regression\">Simple Regression Model</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
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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": [
      "--2020-04-22 07:42:20--  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 0.04s   \n",
      "\n",
      "2020-04-22 07:42:20 (1.69 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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    "\n",
    "<h2 id=\"understanding_data\">Understanding the Data</h2>\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": [
    "<h2 id=\"reading_data\">Reading the data in</h2>"
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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",
       "</table>\n",
       "</div>"
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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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    "<h2 id=\"data_exploration\">Data Exploration</h2>\n",
    "Lets first have a descriptive exploration on our data."
   ]
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   "cell_type": "code",
   "execution_count": 4,
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       "      <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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets select some features to explore more."
   ]
  },
  {
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   "execution_count": 5,
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       "      <th></th>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can plot each of these features:"
   ]
  },
  {
   "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": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 10,
     "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": [
    "# 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",
    "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: "
   ]
  },
  {
   "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": [
    "<h2 id=\"simple_regression\">Simple Regression Model</h2>\n",
    "Linear Regression fits a linear model with coefficients $\\theta = (\\theta_1, ..., \\theta_n)$ 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:  [[39.39090444]]\n",
      "Intercept:  [124.74890719]\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",
    "<ul>\n",
    "    <li> 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.</li>\n",
    "    <li> 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.</li>\n",
    "    <li> Root Mean Squared Error (RMSE): This is the square root of the Mean Square Error. </li>\n",
    "    <li> 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).</li>\n",
    "</ul>"
   ]
  },
  {
   "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: 23.48\n",
      "Residual sum of squares (MSE): 983.87\n",
      "R2-score: 0.69\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "test_y_hat = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_hat - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_hat - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y_hat , test_y) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2>Want to learn more?</h2>\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: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\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 <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
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