Lab-Mulitple-Linear-Regression-Co2.jupyterlite.ipynb

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        "name": "python",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "kernelspec": {
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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# Multiple 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 Multiple Linear Regression\n* Create a model, train it, test it and use the model\n",
      "metadata": {}
    },
    {
      "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=\"#understanding-data\">Understanding the Data</a></li>\n        <li><a href=\"#reading_data\">Reading the Data in</a></li>\n        <li><a href=\"#multiple_regression_model\">Multiple Regression Model</a></li>\n        <li><a href=\"#prediction\">Prediction</a></li>\n        <li><a href=\"#practice\">Practice</a></li>\n    </ol>\n</div>\n<br>\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Importing Needed packages\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "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": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Downloading Data\nwe will use the link, 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
      },
      "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": "\n<h2 id=\"understanding_data\">Understanding the Data</h2>\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- **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": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"reading_data\">Reading the data in</h2>\n",
      "metadata": {}
    },
    {
      "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": {
        "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": {
        "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": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Creating train and test dataset\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": {}
    },
    {
      "cell_type": "code",
      "source": "msk = np.random.rand(len(df)) < 0.8\ntrain = cdf[msk]\ntest = cdf[~msk]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"multiple_regression_model\">Multiple Regression Model</h2>\n",
      "metadata": {}
    },
    {
      "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": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[11.15163236  7.24612619  9.76914259]]\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)\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* 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(\"Mean Squared error(MSE): %.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": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/sklearn/base.py:432: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Mean Squared error(MSE): 580.82\nVariance score: 0.85\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\nfrom sklearn import linear_model\nregr = linear_model.LinearRegression()\nx = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit (x, y)\n# The coefficients\nprint ('Coefficients: ', regr.coef_)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "text": "Coefficients:  [[11.25789656  6.73026043  6.67215679  2.68906443]]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\nx = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\ny = np.asanyarray(test[['CO2EMISSIONS']])\n\nprint(\"Mean Squared error(MSE): %.2f\"\n      % np.mean((y_ - y) ** 2))\nprint('Variance score: %.2f' % regr.score(x, y))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/sklearn/base.py:432: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Mean Squared error(MSE): 584.79\nVariance score: 0.85\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": "The first model (\"y_hat\") fits better. The Mean Squared error(MSE) is lower, and the Variance score is higher.",
      "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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork1047-2023-01-01\">Watson Studio</a>\n\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\n\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  | Made changes in URL |\n| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n|   |   |   |   |\n|   |   |   |   |\n\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}