01_ML0101EN-Reg-Simple-Linear-Regression-Co2__M.ipynb

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      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "prev_pub_hash": "62bb0e28261d2c022e8a185a325cf83c2e3cc914cc7e9f58888934521f20af59"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n    </a>\n</p>\n\n\n# Simple Linear Regression\n\n\nEstimated time needed: **15** minutes\n    \n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n* Use scikit-learn to implement simple Linear Regression\n* Create a model, train it, test it and use the model\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Importing Needed packages\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport pylab as pl\nimport numpy as np\n%matplotlib inline",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-2-76706e26cc54>:2: DeprecationWarning: \nPyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\nbut was not found to be installed on your system.\nIf this would cause problems for you,\nplease provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n        \n  import pandas as pd\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": "### Downloading Data\nTo download the data, we will use !wget to download it from IBM Object Storage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\"",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())\n\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "markdown",
      "source": "\n## Understanding the Data\n\n### `FuelConsumption.csv`:\nWe 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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Reading the data in\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"FuelConsumption.csv\")\npath=\"FuelConsumption.csv\"",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(\"FuelConsumption.csv\")\n\n# take a look at the dataset\ndf.head()\n\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n0       2014  ACURA         ILX      COMPACT         2.0          4   \n1       2014  ACURA         ILX      COMPACT         2.4          4   \n2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n\n  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n0          AS5        Z                   9.9                  6.7   \n1           M6        Z                  11.2                  7.7   \n2          AV7        Z                   6.0                  5.8   \n3          AS6        Z                  12.7                  9.1   \n4          AS6        Z                  12.1                  8.7   \n\n   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n0                   8.5                        33           196  \n1                   9.6                        29           221  \n2                   5.9                        48           136  \n3                  11.1                        25           255  \n4                  10.6                        27           244  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MODELYEAR</th>\n      <th>MAKE</th>\n      <th>MODEL</th>\n      <th>VEHICLECLASS</th>\n      <th>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>TRANSMISSION</th>\n      <th>FUELTYPE</th>\n      <th>FUELCONSUMPTION_CITY</th>\n      <th>FUELCONSUMPTION_HWY</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>FUELCONSUMPTION_COMB_MPG</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.0</td>\n      <td>4</td>\n      <td>AS5</td>\n      <td>Z</td>\n      <td>9.9</td>\n      <td>6.7</td>\n      <td>8.5</td>\n      <td>33</td>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.4</td>\n      <td>4</td>\n      <td>M6</td>\n      <td>Z</td>\n      <td>11.2</td>\n      <td>7.7</td>\n      <td>9.6</td>\n      <td>29</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>ILX HYBRID</td>\n      <td>COMPACT</td>\n      <td>1.5</td>\n      <td>4</td>\n      <td>AV7</td>\n      <td>Z</td>\n      <td>6.0</td>\n      <td>5.8</td>\n      <td>5.9</td>\n      <td>48</td>\n      <td>136</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>MDX 4WD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.7</td>\n      <td>9.1</td>\n      <td>11.1</td>\n      <td>25</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014</td>\n      <td>ACURA</td>\n      <td>RDX AWD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.1</td>\n      <td>8.7</td>\n      <td>10.6</td>\n      <td>27</td>\n      <td>244</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": "### Data Exploration\nLet's first have a descriptive exploration on our data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# summarize the data\ndf.describe()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\ncount     1067.0  1067.000000  1067.000000           1067.000000   \nmean      2014.0     3.346298     5.794752             13.296532   \nstd          0.0     1.415895     1.797447              4.101253   \nmin       2014.0     1.000000     3.000000              4.600000   \n25%       2014.0     2.000000     4.000000             10.250000   \n50%       2014.0     3.400000     6.000000             12.600000   \n75%       2014.0     4.300000     8.000000             15.550000   \nmax       2014.0     8.400000    12.000000             30.200000   \n\n       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\ncount          1067.000000           1067.000000               1067.000000   \nmean              9.474602             11.580881                 26.441425   \nstd               2.794510              3.485595                  7.468702   \nmin               4.900000              4.700000                 11.000000   \n25%               7.500000              9.000000                 21.000000   \n50%               8.800000             10.900000                 26.000000   \n75%              10.850000             13.350000                 31.000000   \nmax              20.500000             25.800000                 60.000000   \n\n       CO2EMISSIONS  \ncount   1067.000000  \nmean     256.228679  \nstd       63.372304  \nmin      108.000000  \n25%      207.000000  \n50%      251.000000  \n75%      294.000000  \nmax      488.000000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": "Let's select some features to explore more.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head(9)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n0         2.0          4                   8.5           196\n1         2.4          4                   9.6           221\n2         1.5          4                   5.9           136\n3         3.5          6                  11.1           255\n4         3.5          6                  10.6           244\n5         3.5          6                  10.0           230\n6         3.5          6                  10.1           232\n7         3.7          6                  11.1           255\n8         3.7          6                  11.6           267",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "We can plot each of these features:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\nviz.hist()\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 4 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9
    },
    {
      "cell_type": "markdown",
      "source": "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"FUELCONSUMPTION_COMB\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": "## Practice\nPlot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\n\nplt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Cylinders\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python    \nplt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Cylinders\")\nplt.ylabel(\"Emission\")\nplt.show()\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Creating train and test dataset\nTrain/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \nThis 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 model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n\nThis means that we know the outcome of each data point in the testing dataset, making it great to test with! 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\nLet's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "msk = np.random.rand(len(df)) < 0.8\ntrain = cdf[msk]\ntest = cdf[~msk]",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "### Simple Regression Model\nLinear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Train data distribution\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "#### Modeling\nUsing sklearn package to model data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn import linear_model\nregr = linear_model.LinearRegression()\ntrain_x = np.asanyarray(train[['ENGINESIZE']])\ntrain_y = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit(train_x, train_y)\n# The coefficients\nprint ('Coefficients: ', regr.coef_)\nprint ('Intercept: ',regr.intercept_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[39.38003509]]\nIntercept:  [124.13577012]\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \nGiven 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. \nNotice that all of the data must be available to traverse and calculate the parameters.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Plot outputs\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the fit line over the data:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Emission')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "#### Evaluation\nWe 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\nThere 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\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\n* Root Mean Squared Error (RMSE). \n\n* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import r2_score\n\ntest_x = np.asanyarray(test[['ENGINESIZE']])\ntest_y = np.asanyarray(test[['CO2EMISSIONS']])\ntest_y_ = regr.predict(test_x)\n\nprint(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\nprint(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\nprint(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean absolute error: 24.82\nResidual sum of squares (MSE): 1053.58\nR2-score: 0.76\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": "## Exercise\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n\nStart by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "train_x = train[[\"FUELCONSUMPTION_COMB\"]] #ADD CODE\n\ntest_x = test[[\"FUELCONSUMPTION_COMB\"]] #ADD CODE\n\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python    \ntrain_x = train[[\"FUELCONSUMPTION_COMB\"]]\n\ntest_x = test[[\"FUELCONSUMPTION_COMB\"]]\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "regr = linear_model.LinearRegression()\n\n#ADD CODE\nregr = linear_model.LinearRegression()\n\nregr.fit(train_x, train_y)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()",
            "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python    \nregr = linear_model.LinearRegression()\n\nregr.fit(train_x, train_y)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the predictions using the model's `predict` function and the `test_x` data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "predictions = regr.predict(test_x) #ADD CODE",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 20
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python    \npredictions = regr.predict(test_x)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#ADD CODE\nprint(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean Absolute Error: 21.71\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python    \nprint(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n\n## Author\n\nSaeed Aghabozorgi\n\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n\nAzim Hirjani\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n\n<!--\n## Change Log\n\n\n|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n|---|---|---|---|\n| 2020-11-03 | 2.1  | Lakshmi Holla  |  Changed URL of the csv |\n| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n|   |   |   |   |\n|   |   |   |   |\n--!>\n\n\n",
      "metadata": {}
    }
  ]
}