c09-machine-learning-with-python/res/NB2-Mulitple-Linear-Regression-Co2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" 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# Multiple Linear Regression\n\nEstimated time needed: **15** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Use scikit-learn to implement Multiple Linear Regression\n*   Create a model, train it, test it and use the model\n",
      "metadata": {
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    {
      "cell_type": "markdown",
      "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=\"https://#understanding-data\">Understanding the Data</a></li>\n        <li><a href=\"https://#reading_data\">Reading the Data in</a></li>\n        <li><a href=\"https://#multiple_regression_model\">Multiple Regression Model</a></li>\n        <li><a href=\"https://#prediction\">Prediction</a></li>\n        <li><a href=\"https://#practice\">Practice</a></li>\n    </ol>\n</div>\n<br>\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Importing Needed packages\n",
      "metadata": {
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        }
      }
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "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": {
        "button": false,
        "new_sheet": false,
        "run_control": {
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        },
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Downloading Data\n\nwe will use the link, we will use !wget to download it from IBM Object Storage.\n",
      "metadata": {
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        "new_sheet": false,
        "run_control": {
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        }
      }
    },
    {
      "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": {
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        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "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())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"understanding_data\">Understanding the Data</h2>\n\n### `FuelConsumption.csv`:\n\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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\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*   **FUELTYPE** e.g. z\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": {
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        "new_sheet": false,
        "run_control": {
          "read_only": false
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      }
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"reading_data\">Reading the data in</h2>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"FuelConsumption.csv\")\npath=\"FuelConsumption.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(path)\n\n# take a look at the dataset\ndf.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 6,
      "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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's select some features that we want to use for regression.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head(9)",
      "metadata": {
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        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n0         2.0          4                   9.9                  6.7   \n1         2.4          4                  11.2                  7.7   \n2         1.5          4                   6.0                  5.8   \n3         3.5          6                  12.7                  9.1   \n4         3.5          6                  12.1                  8.7   \n5         3.5          6                  11.9                  7.7   \n6         3.5          6                  11.8                  8.1   \n7         3.7          6                  12.8                  9.0   \n8         3.7          6                  13.4                  9.5   \n\n   FUELCONSUMPTION_COMB  CO2EMISSIONS  \n0                   8.5           196  \n1                   9.6           221  \n2                   5.9           136  \n3                  11.1           255  \n4                  10.6           244  \n5                  10.0           230  \n6                  10.1           232  \n7                  11.1           255  \n8                  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_CITY</th>\n      <th>FUELCONSUMPTION_HWY</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>9.9</td>\n      <td>6.7</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>11.2</td>\n      <td>7.7</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>6.0</td>\n      <td>5.8</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>12.7</td>\n      <td>9.1</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>12.1</td>\n      <td>8.7</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>11.9</td>\n      <td>7.7</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>11.8</td>\n      <td>8.1</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>12.8</td>\n      <td>9.0</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>13.4</td>\n      <td>9.5</td>\n      <td>11.6</td>\n      <td>267</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's plot Emission values with respect to Engine size:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
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      "execution_count": 8,
      "outputs": [
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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Creating train and test dataset\n\nTrain/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.\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\nWe 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. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the  **np.random.rand()** function:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "msk = np.random.rand(len(df)) < 0.8\ntrain = cdf[msk]\ntest = cdf[~msk]",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "#### Train data distribution\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x49b0648>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"multiple_regression_model\">Multiple Regression Model</h2>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "In reality, there are multiple variables that impact the co2emission. When more than one independent variable is present, the process is called multiple linear regression. An example of multiple linear regression is predicting co2emission using the features FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that multiple linear regression model is the extension of the simple linear regression model.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn import linear_model\nregr = linear_model.LinearRegression()\nx = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\ny = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit (x, y)\n# The coefficients\nprint ('Coefficients: ', regr.coef_)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[11.34619284  7.04980104  9.59200092]]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "As mentioned before, **Coefficient** and **Intercept**  are the parameters of the fitted line.\nGiven that it is a multiple linear regression model with 3 parameters and that the parameters are the intercept and coefficients of the hyperplane, sklearn can estimate them from our data. Scikit-learn uses plain Ordinary Least Squares method to solve this problem.\n\n#### Ordinary Least Squares (OLS)\n\nOLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ($\\hat{y}$) over all samples in the dataset.\n\nOLS can find the best parameters using of the following methods:\n\n*   Solving the model parameters analytically using closed-form equations\n*   Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"prediction\">Prediction</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\nx = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\ny = np.asanyarray(test[['CO2EMISSIONS']])\nprint(\"Residual sum of squares: %.2f\"\n      % np.mean((y_hat - y) ** 2))\n\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % regr.score(x, y))",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.10/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Residual sum of squares: 604.13\nVariance score: 0.87\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Explained variance regression score:**\\\nLet $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n\n$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var{ y - \\hat{y}}}{Var{y}}$\\\nThe best possible score is 1.0, the lower values are worse.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"practice\">Practice</h2>\nTry to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\n\nx1 = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny1 = np.asanyarray(train[['CO2EMISSIONS']])\nregr1 = linear_model.LinearRegression()\nregr1.fit(x1, y1)\nprint ('Coefficients: ', regr1.coef_)\n\nx2 = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny_ = regr.predict(x2)\ny2 = np.asanyarray(test[['CO2EMISSIONS']])\n\nprint(\"Residual sum of squares: %.2f\"% np.mean((y_ - y2) ** 2))\nprint('Variance score: %.2f' % regr.score(x2, y2))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[11.35871953  7.03516163  5.30561739  4.27331185]]\nResidual sum of squares: 604.27\nVariance score: 0.87\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nregr = linear_model.LinearRegression()\nx = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit (x, y)\nprint ('Coefficients: ', regr.coef_)\ny_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\nx = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny = np.asanyarray(test[['CO2EMISSIONS']])\nprint(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\nprint('Variance score: %.2f' % regr.score(x, y))\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Want to learn more?</h2>\n\nIBM 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=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n\nAlso, 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://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n| ----------------- | ------- | ---------- | ---------------------------------- |\n| 2020-11-03        | 2.1     | Lakshmi    | Made changes in URL                |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab |\n|                   |         |            |                                    |\n|                   |         |            |                                    |\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}