{
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      "language": "python"
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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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# Polynomial 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 Polynomial Regression\n*   Create a model, train it, test it and use the model\n",
      "metadata": {
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        "new_sheet": false,
        "run_control": {
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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://#download_data\">Downloading Data</a></li>\n        <li><a href=\"https://#polynomial_regression\">Polynomial regression</a></li>\n        <li><a href=\"https://#evaluation\">Evaluation</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": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "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\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "#This function will download the dataset into your browser \n\nfrom 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": 2,
      "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\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"download_data\">Downloading Data</h2>\nTo download the data, we will use !wget to download it from IBM Object Storage.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "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": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "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](https://www.ibm.com/us-en/cloud/object-storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Understanding the Data\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*   **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": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## Reading the data in\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"FuelConsumption.csv\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(\"FuelConsumption.csv\")\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_COMB','CO2EMISSIONS']]\ncdf.head(9)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "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": {}
        }
      ]
    },
    {
      "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": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4050238>",
            "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": "#### 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.\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": "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n\nIn essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n\n$$y = b + \\theta\\_1  x + \\theta\\_2 x^2$$\n\nNow, the question is: how we can fit our data on this equation while we have only x values, such as **Engine Size**?\nWell, we can create a few additional features: 1, $x$, and $x^2$.\n\n**PolynomialFeatures()** function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, *ENGINESIZE*. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import PolynomialFeatures\nfrom sklearn import linear_model\ntrain_x = np.asanyarray(train[['ENGINESIZE']])\ntrain_y = np.asanyarray(train[['CO2EMISSIONS']])\n\ntest_x = np.asanyarray(test[['ENGINESIZE']])\ntest_y = np.asanyarray(test[['CO2EMISSIONS']])\n\n\npoly = PolynomialFeatures(degree=2)\ntrain_x_poly = poly.fit_transform(train_x)\ntrain_x_poly",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[ 1.  ,  2.  ,  4.  ],\n       [ 1.  ,  1.5 ,  2.25],\n       [ 1.  ,  3.5 , 12.25],\n       ...,\n       [ 1.  ,  3.2 , 10.24],\n       [ 1.  ,  3.2 , 10.24],\n       [ 1.  ,  3.2 , 10.24]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**fit_transform** takes our x values, and output a list of our data raised from power of 0 to power of 2 (since we set the degree of our polynomial to 2).\n\nThe equation and the sample example is displayed below.\n\n$$\n\\begin{bmatrix}\nv\\_1\\\\\\\\\nv\\_2\\\\\n\\vdots\\\\\nv_n\n\\end{bmatrix}\\longrightarrow \\begin{bmatrix}\n\\[ 1 & v\\_1 & v\\_1^2]\\\\\n\\[ 1 & v\\_2 & v\\_2^2]\\\\\n\\vdots & \\vdots & \\vdots\\\\\n\\[ 1 & v_n & v_n^2]\n\\end{bmatrix}\n$$\n\n$$\n\\begin{bmatrix}\n2.\\\\\n2.4\\\\\n1.5\\\\\n\\vdots\n\\end{bmatrix} \\longrightarrow \\begin{bmatrix}\n\\[ 1 & 2. & 4.]\\\\\n\\[ 1 & 2.4 & 5.76]\\\\\n\\[ 1 & 1.5 & 2.25]\\\\\n\\vdots & \\vdots & \\vdots\\\\\n\\end{bmatrix}\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "It looks like feature sets for multiple linear regression analysis, right? Yes. It Does.\nIndeed, Polynomial regression is a special case of linear regression, with the main idea of how do you select your features. Just consider replacing the  $x$ with $x\\_1$, $x\\_1^2$ with $x\\_2$, and so on. Then the 2nd degree equation would be turn into:\n\n$$y = b + \\theta\\_1  x\\_1 + \\theta\\_2 x\\_2$$\n\nNow, we can deal with it as a 'linear regression' problem. Therefore, this polynomial regression is considered to be a special case of traditional multiple linear regression. So, you can use the same mechanism as linear regression to solve such problems.\n\nso we can use **LinearRegression()** function to solve it:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "clf = linear_model.LinearRegression()\ntrain_y_ = clf.fit(train_x_poly, train_y)\n# The coefficients\nprint ('Coefficients: ', clf.coef_)\nprint ('Intercept: ',clf.intercept_)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[ 0.         48.40492157 -1.13881415]]\nIntercept:  [109.16654165]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "As mentioned before, **Coefficient** and **Intercept** , are the parameters of the fit curvy line.\nGiven that it is a typical multiple linear regression, with 3 parameters, and knowing that the parameters are the intercept and coefficients of hyperplane, sklearn has estimated them from our new set of feature sets. Lets plot it:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nXX = np.arange(0.0, 10.0, 0.1)\nyy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)\nplt.plot(XX, yy, '-r' )\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "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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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"evaluation\">Evaluation</h2>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import r2_score\n\ntest_x_poly = poly.transform(test_x)\ntest_y_ = clf.predict(test_x_poly)\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
      },
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean absolute error: 23.42\nResidual sum of squares (MSE): 982.91\nR2-score: 0.75\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"practice\">Practice</h2>\nTry to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\npoly3 = PolynomialFeatures(degree=3)\ntrain_x_poly3 = poly3.fit_transform(train_x)\ntrain_x_poly3",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[ 1.   ,  2.   ,  4.   ,  8.   ],\n       [ 1.   ,  1.5  ,  2.25 ,  3.375],\n       [ 1.   ,  3.5  , 12.25 , 42.875],\n       ...,\n       [ 1.   ,  3.2  , 10.24 , 32.768],\n       [ 1.   ,  3.2  , 10.24 , 32.768],\n       [ 1.   ,  3.2  , 10.24 , 32.768]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\npoly3 = PolynomialFeatures(degree=3)\ntrain_x_poly3 = poly3.fit_transform(train_x)\nclf3 = linear_model.LinearRegression()\ntrain_y3_ = clf3.fit(train_x_poly3, train_y)\n\n# The coefficients\nprint ('Coefficients: ', clf3.coef_)\nprint ('Intercept: ',clf3.intercept_)\nplt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nXX = np.arange(0.0, 10.0, 0.1)\nyy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\nplt.plot(XX, yy, '-r' )\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\ntest_x_poly3 = poly3.transform(test_x)\ntest_y3_ = clf3.predict(test_x_poly3)\nprint(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\nprint(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\nprint(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "clf3 = linear_model.LinearRegression()\ntrain_y3_ = clf3.fit(train_x_poly3, train_y)\n# The coefficients\nprint ('Coefficients: ', clf3.coef_)\nprint ('Intercept: ',clf3.intercept_)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[ 0.         31.59378342  3.55514335 -0.3951294 ]]\nIntercept:  [126.85792858]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nXX = np.arange(0.0, 10.0, 0.1)\nyy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX+ clf3.coef_[0][2]*np.power(XX, 2)+clf3.coef_[0][3]*np.power(XX,3)\nplt.plot(XX, yy, '-r' )\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "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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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "test_x_poly3 = poly3.transform(test_x)\ntest_y3_ = clf3.predict(test_x_poly3)\n\nprint(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\nprint(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\nprint(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "name": "stdout",
          "text": "Mean absolute error: 23.32\nResidual sum of squares (MSE): 972.99\nR2-score: 0.76\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": {}
    },
    {
      "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| 2021-01-11        | 2.3     | Lakshmi    | Changed R2-score calculation in polynomial regression |\n| 2020-11-04        | 2.2     | Lakshmi    | Made changes in markdown of equations                 |\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": {}
    }
  ]
}