ML01-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

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 "cells": [
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Importing Needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "new_sheet": false,
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3296, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-e22086e0df22>\", line 1, in <module>\n",
      "    import matplotlib.pyplot as plt\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/matplotlib/__init__.py\", line 1099, in <module>\n",
      "    rcParams = rc_params()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/matplotlib/__init__.py\", line 940, in rc_params\n",
      "    fname = matplotlib_fname()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/matplotlib/__init__.py\", line 746, in matplotlib_fname\n",
      "    for fname in gen_candidates():\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/matplotlib/__init__.py\", line 735, in gen_candidates\n",
      "    yield os.path.join(os.getcwd(), 'matplotlibrc')\n",
      "FileNotFoundError: [Errno 2] No such file or directory\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2033, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'FileNotFoundError' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1095, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 347, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"/opt/conda/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"/opt/conda/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
      "    filename = getsourcefile(frame) or getfile(frame)\n",
      "  File \"/opt/conda/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
      "  File \"/opt/conda/lib/python3.7/inspect.py\", line 725, in getmodule\n",
      "    file = getabsfile(object, _filename)\n",
      "  File \"/opt/conda/lib/python3.7/inspect.py\", line 709, in getabsfile\n",
      "    return os.path.normcase(os.path.abspath(_filename))\n",
      "  File \"/opt/conda/lib/python3.7/posixpath.py\", line 383, in abspath\n",
      "    cwd = os.getcwd()\n",
      "FileNotFoundError: [Errno 2] No such file or directory\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m"
     ]
    }
   ],
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sh: 0: getcwd() failed: No such file or directory\n",
      "--2019-06-08 22:42:52--  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: ‘./data/FuelConsumption.csv’\n",
      "\n",
      "./data/FuelConsumpt 100%[===================>]  70.93K  --.-KB/s    in 0.1s    \n",
      "\n",
      "2019-06-08 22:42:52 (653 KB/s) - ‘./data/FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O \"./data/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)"
   ]
  },
  {
   "cell_type": "markdown",
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Reading the data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "deletable": true,
    "new_sheet": false,
    "run_control": {
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    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Data Exploration\n",
    "Lets first have a descriptive exploration on our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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   "outputs": [
    {
     "data": {
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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>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 24,
     "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": 25,
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    "button": false,
    "collapsed": true,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {
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    "collapsed": true,
    "deletable": true,
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   "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": 27,
   "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": 28,
   "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": 29,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# write your code here\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Double-click __here__ for the solution.\n",
    "\n",
    "   \n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\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": 31,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19085501, 0.96660093, 0.65719847, ..., 0.61516015, 0.50213734,\n",
       "       0.78260858])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### intereesting way to split data in training and testing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True, False,  True])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "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": 35,
   "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(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": 36,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.71595173]]\n",
      "Intercept:  [126.75269012]\n"
     ]
    }
   ],
   "source": [
    "# initialize thee modeel object \n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# prepare input data as number arrays\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "\n",
    "# fit thee model, this is done inplace\n",
    "regr.fit (train_x, train_y)\n",
    "\n",
    "# get 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": 38,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc07864b1d0>"
      ]
     },
     "execution_count": 38,
     "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "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": 40,
   "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: 23.29\n",
      "Residual sum of squares (MSE): 946.07\n",
      "R2-score: 0.72\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "\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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