IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork_jupyterlite_files_DA0101EN-4-Review-Model-Development.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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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%204/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Model Development\n\nEstimated time needed: **30** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Develop prediction models\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>In this section, we will develop several models that will predict the price of the car using the variables or features. This is just an estimate but should give us an objective idea of how much the car should cost.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Some questions we want to ask in this module\n\n<ul>\n    <li>Do I know if the dealer is offering fair value for my trade-in?</li>\n    <li>Do I know if I put a fair value on my car?</li>\n</ul>\n<p>In data analytics, we often use <b>Model Development</b> to help us predict future observations from the data we have.</p>\n\n<p>A model will help us understand the exact relationship between different variables and how these variables are used to predict the result.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Setup</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import libraries:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "you are running the lab in your  browser, so we will install the libraries using `piplite`\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#you are running the lab in your  browser, so we will install the libraries using ``piplite``\nimport piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['scipy'])\nawait piplite.install(['seaborn'])\nawait piplite.install(['scikit-learn'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n#install specific version of libraries used in lab\n#! mamba install pandas==1.3.3-y\n#! mamba install numpy=1.21.2-y\n#! mamba install sklearn=0.20.1-y",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function will download the dataset into your browser\n",
      "metadata": {}
    },
    {
      "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": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This dataset was hosted on IBM Cloud object. Click <a href=\"https://cocl.us/DA101EN_object_storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\">HERE</a> for free storage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you will need to download the dataset; if you are running locally, please comment out the following\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#you will need to download the dataset; if you are running locally, please comment out the following \nawait download(path, \"auto.csv\")\npath=\"auto.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Load the data and store it in dataframe `df`:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "\ndf = pd.read_csv(path)\nprint(df.shape)\ndf.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "text": "(201, 29)\n",
          "output_type": "stream"
        },
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   symboling  normalized-losses         make aspiration num-of-doors  \\\n0          3                122  alfa-romero        std          two   \n1          3                122  alfa-romero        std          two   \n2          1                122  alfa-romero        std          two   \n3          2                164         audi        std         four   \n4          2                164         audi        std         four   \n\n    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n0  convertible          rwd           front        88.6  0.811148  ...   \n1  convertible          rwd           front        88.6  0.811148  ...   \n2    hatchback          rwd           front        94.5  0.822681  ...   \n3        sedan          fwd           front        99.8  0.848630  ...   \n4        sedan          4wd           front        99.4  0.848630  ...   \n\n   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n0                9.0       111.0    5000.0       21          27  13495.0   \n1                9.0       111.0    5000.0       21          27  16500.0   \n2                9.0       154.0    5000.0       19          26  16500.0   \n3               10.0       102.0    5500.0       24          30  13950.0   \n4                8.0       115.0    5500.0       18          22  17450.0   \n\n  city-L/100km  horsepower-binned  diesel  gas  \n0    11.190476             Medium       0    1  \n1    11.190476             Medium       0    1  \n2    12.368421             Medium       0    1  \n3     9.791667             Medium       0    1  \n4    13.055556             Medium       0    1  \n\n[5 rows x 29 columns]",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>make</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>...</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>horsepower-binned</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>94.5</td>\n      <td>0.822681</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>154.0</td>\n      <td>5000.0</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500.0</td>\n      <td>12.368421</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>10.0</td>\n      <td>102.0</td>\n      <td>5500.0</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950.0</td>\n      <td>9.791667</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>4wd</td>\n      <td>front</td>\n      <td>99.4</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>8.0</td>\n      <td>115.0</td>\n      <td>5500.0</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450.0</td>\n      <td>13.055556</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2>1. Linear Regression and Multiple Linear Regression</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Linear Regression</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>One example of a Data  Model that we will be using is:</p>\n<b>Simple Linear Regression</b>\n\n<br>\n<p>Simple Linear Regression is a method to help us understand the relationship between two variables:</p>\n<ul>\n    <li>The predictor/independent variable (X)</li>\n    <li>The response/dependent variable (that we want to predict)(Y)</li>\n</ul>\n\n<p>The result of Linear Regression is a <b>linear function</b> that predicts the response (dependent) variable as a function of the predictor (independent) variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\nY: Response \\ Variable\\\\\\\\\\\\\\\\\\\\\nX: Predictor \\ Variables\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<b>Linear Function</b>\n$$\nYhat = a + b  X\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<ul>\n    <li>a refers to the <b>intercept</b> of the regression line, in other words: the value of Y when X is 0</li>\n    <li>b refers to the <b>slope</b> of the regression line, in other words: the value with which Y changes when X increases by 1 unit</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Let's load the modules for linear regression:</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.linear_model import LinearRegression",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Create the linear regression object:</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm = LinearRegression()\nlm",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>How could \"highway-mpg\" help us predict car price?</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "For this example, we want to look at how highway-mpg can help us predict car price.\nUsing simple linear regression, we will create a linear function with \"highway-mpg\" as the predictor variable and the \"price\" as the response variable.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X = df[['highway-mpg']]\nY = df['price']",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Fit the linear model using highway-mpg:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.fit(X,Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can output a prediction:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Yhat=lm.predict(X)\nYhat[0:5]   ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([16236.50464347, 16236.50464347, 17058.23802179, 13771.3045085 ,\n       20345.17153508])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>What is the value of the intercept (a)?</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.intercept_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "38423.30585815743"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>What is the value of the slope (b)?</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.coef_",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([-821.73337832])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>What is the final estimated linear model we get?</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "As we saw above, we should get a final linear model with the structure:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\nYhat = a + b  X\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Plugging in the actual values we get:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<b>Price</b> = 38423.31 - 821.73 x <b>highway-mpg</b>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question #1 a): </h1>\n\n<b>Create a linear regression object called \"lm1\".</b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm1 = LinearRegression()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nlm1 = LinearRegression()\nlm1\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question #1 b): </h1>\n\n<b>Train the model using \"engine-size\" as the independent variable and \"price\" as the dependent variable?</b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm1.fit(df[['engine-size']], df[['price']])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nlm1.fit(df[['engine-size']], df[['price']])\nlm1\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question #1 c):</h1>\n\n<b>Find the slope and intercept of the model.</b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Slope</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm1.coef_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[166.86001569]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# Slope \nlm1.coef_\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Intercept</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm1.intercept_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([-7963.33890628])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# Intercept\nlm1.intercept_\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(f'Y = {lm1.coef_} * X + ({lm1.intercept_})')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "name": "stdout",
          "text": "Y = [[166.86001569]] * X + ([-7963.33890628])\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question #1 d): </h1>\n\n<b>What is the equation of the predicted line? You can use x and yhat or \"engine-size\" or \"price\".</b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \n# using X and Y  \npriceHat = lm1.coef_ * df[['engine-size']] + lm1.intercept_\npriceHat",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "      engine-size\n0    13728.463134\n1    13728.463134\n2    17399.383479\n3    10224.402804\n4    14729.623228\n..            ...\n196  15563.923306\n197  15563.923306\n198  20903.443808\n199  16231.363369\n200  15563.923306\n\n[201 rows x 1 columns]",
            "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>engine-size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13728.463134</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13728.463134</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17399.383479</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10224.402804</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14729.623228</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>15563.923306</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>15563.923306</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>20903.443808</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>16231.363369</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>15563.923306</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# using X and Y  \nYhat=-7963.34 + 166.86*X\n\nPrice=-7963.34 + 166.86*df['engine-size']\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Multiple Linear Regression</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>What if we want to predict car price using more than one variable?</p>\n\n<p>If we want to use more variables in our model to predict car price, we can use <b>Multiple Linear Regression</b>.\nMultiple Linear Regression is very similar to Simple Linear Regression, but this method is used to explain the relationship between one continuous response (dependent) variable and <b>two or more</b> predictor (independent) variables.\nMost of the real-world regression models involve multiple predictors. We will illustrate the structure by using four predictor variables, but these results can generalize to any integer:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\nY: Response \\ Variable\\\\\\\\\\\\\\\\\\\\\nX\\_1 :Predictor\\ Variable \\ 1\\\\\\\\\nX\\_2: Predictor\\ Variable \\ 2\\\\\\\\\nX\\_3: Predictor\\ Variable \\ 3\\\\\\\\\nX\\_4: Predictor\\ Variable \\ 4\\\\\\\\\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\na: intercept\\\\\\\\\\\\\\\\\\\\\nb\\_1 :coefficients \\ of\\ Variable \\ 1\\\\\\\\\nb\\_2: coefficients \\ of\\ Variable \\ 2\\\\\\\\\nb\\_3: coefficients \\ of\\ Variable \\ 3\\\\\\\\\nb\\_4: coefficients \\ of\\ Variable \\ 4\\\\\\\\\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The equation is given by:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\nYhat = a + b\\_1 X\\_1 + b\\_2 X\\_2 + b\\_3 X\\_3 + b\\_4 X\\_4\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>From the previous section  we know that other good predictors of price could be:</p>\n<ul>\n    <li>Horsepower</li>\n    <li>Curb-weight</li>\n    <li>Engine-size</li>\n    <li>Highway-mpg</li>\n</ul>\nLet's develop a model using these variables as the predictor variables.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Fit the linear model using the four above-mentioned variables.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.fit(Z, df['price'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "What is the value of the intercept(a)?\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.intercept_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "-15806.62462632922"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "What are the values of the coefficients (b1, b2, b3, b4)?\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.coef_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([53.49574423,  4.70770099, 81.53026382, 36.05748882])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "What is the final estimated linear model that we get?\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "As we saw above, we should get a final linear function with the structure:\n\n$$\nYhat = a + b\\_1 X\\_1 + b\\_2 X\\_2 + b\\_3 X\\_3 + b\\_4 X\\_4\n$$\n\nWhat is the linear function we get in this example?\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<b>Price</b> = -15678.742628061467 + 52.65851272 x <b>horsepower</b> + 4.69878948 x <b>curb-weight</b> + 81.95906216 x <b>engine-size</b> + 33.58258185 x <b>highway-mpg</b>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question  #2 a): </h1>\nCreate and train a Multiple Linear Regression model \"lm2\" where the response variable is \"price\", and the predictor variable is \"normalized-losses\" and  \"highway-mpg\".\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm2 = LinearRegression()\nlm2.fit(df[['normalized-losses', 'highway-mpg']], df[['price']])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nlm2 = LinearRegression()\nlm2.fit(df[['normalized-losses' , 'highway-mpg']],df['price'])\n\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question  #2 b): </h1>\n<b>Find the coefficient of the model.</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nlm2.coef_",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[   1.49789586, -820.45434016]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nlm2.coef_\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>2. Model Evaluation Using Visualization</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now that we've developed some models, how do we evaluate our models and choose the best one? One way to do this is by using a visualization.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the visualization package, seaborn:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# import the visualization package: seaborn\nimport seaborn as sns\n%matplotlib inline ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Regression Plot</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>When it comes to simple linear regression, an excellent way to visualize the fit of our model is by using <b>regression plots</b>.</p>\n\n<p>This plot will show a combination of a scattered data points (a <b>scatterplot</b>), as well as the fitted <b>linear regression</b> line going through the data. This will give us a reasonable estimate of the relationship between the two variables, the strength of the correlation, as well as the direction (positive or negative correlation).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's visualize **highway-mpg** as potential predictor variable of price:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "width = 12\nheight = 10\nplt.figure(figsize=(width, height))\nsns.regplot(x=\"highway-mpg\", y=\"price\", data=df)\nplt.ylim(0,)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 35,
      "outputs": [
        {
          "execution_count": 35,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 48165.313953297344)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x1000 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>We can see from this plot that price is negatively correlated to highway-mpg since the regression slope is negative.\n\nOne thing to keep in mind when looking at a regression plot is to pay attention to how scattered the data points are around the regression line. This will give you a good indication of the variance of the data and whether a linear model would be the best fit or not. If the data is too far off from the line, this linear model might not be the best model for this data.\n\nLet's compare this plot to the regression plot of \"peak-rpm\".</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(width, height))\nsns.regplot(x=\"peak-rpm\", y=\"price\", data=df)\nplt.ylim(0,)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 47414.1)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x1000 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Comparing the regression plot of \"peak-rpm\" and \"highway-mpg\", we see that the points for \"highway-mpg\" are much closer to the generated line and, on average, decrease. The points for \"peak-rpm\" have more spread around the predicted line and it is much harder to determine if the points are decreasing or increasing as the \"peak-rpm\" increases.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question #3:</h1>\n<b>Given the regression plots above, is \"peak-rpm\" or \"highway-mpg\" more strongly correlated with \"price\"? Use the method  \".corr()\" to verify your answer.</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \ndf[['peak-rpm', 'highway-mpg', 'price']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "execution_count": 41,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             peak-rpm  highway-mpg     price\npeak-rpm     1.000000    -0.058598 -0.101616\nhighway-mpg -0.058598     1.000000 -0.704692\nprice       -0.101616    -0.704692  1.000000",
            "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>peak-rpm</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>peak-rpm</th>\n      <td>1.000000</td>\n      <td>-0.058598</td>\n      <td>-0.101616</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>-0.058598</td>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.101616</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# The variable \"highway-mpg\" has a stronger correlation with \"price\", it is approximate -0.704692  compared to \"peak-rpm\" which is approximate -0.101616. You can verify it using the following command:\n\ndf[[\"peak-rpm\",\"highway-mpg\",\"price\"]].corr()\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Residual Plot</h3>\n\n<p>A good way to visualize the variance of the data is to use a residual plot.</p>\n\n<p>What is a <b>residual</b>?</p>\n\n<p>The difference between the observed value (y) and the predicted value (Yhat) is called the residual (e). When we look at a regression plot, the residual is the distance from the data point to the fitted regression line.</p>\n\n<p>So what is a <b>residual plot</b>?</p>\n\n<p>A residual plot is a graph that shows the residuals on the vertical y-axis and the independent variable on the horizontal x-axis.</p>\n\n<p>What do we pay attention to when looking at a residual plot?</p>\n\n<p>We look at the spread of the residuals:</p>\n\n<p>- If the points in a residual plot are <b>randomly spread out around the x-axis</b>, then a <b>linear model is appropriate</b> for the data.\n\nWhy is that? Randomly spread out residuals means that the variance is constant, and thus the linear model is a good fit for this data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "width = 12\nheight = 10\nplt.figure(figsize=(width, height))\nsns.residplot(x=df['highway-mpg'],y=df['price'])\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x6d119b0>",
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<i>What is this plot telling us?</i>\n\n<p>We can see from this residual plot that the residuals are not randomly spread around the x-axis, leading us to believe that maybe a non-linear model is more appropriate for this data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Multiple Linear Regression</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>How do we visualize a model for Multiple Linear Regression? This gets a bit more complicated because you can't visualize it with regression or residual plot.</p>\n\n<p>One way to look at the fit of the model is by looking at the <b>distribution plot</b>. We can look at the distribution of the fitted values that result from the model and compare it to the distribution of the actual values.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "First, let's make a prediction:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_hat = lm.predict(Z)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(width, height))\n\n\n#ax1 = sns.distplot(df['price'], hist=False, color=\"r\", label=\"Actual Value\")\n#sns.distplot(Y_hat, hist=False, color=\"b\", label=\"Fitted Values\" , ax=ax1)\nax1 = sns.kdeplot(df['price'], color=\"r\", label=\"Actual Value\")\nsns.kdeplot(Y_hat, color=\"b\", label=\"Fitted Values\" , ax=ax1)\n\nplt.title('Actual vs Fitted Values for Price')\nplt.xlabel('Price (in dollars)')\nplt.ylabel('Proportion of Cars')\n\nplt.show()\nplt.close()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x69c2190>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>We can see that the fitted values are reasonably close to the actual values since the two distributions overlap a bit. However, there is definitely some room for improvement.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>3. Polynomial Regression and Pipelines</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p><b>Polynomial regression</b> is a particular case of the general linear regression model or multiple linear regression models.</p> \n<p>We get non-linear relationships by squaring or setting higher-order terms of the predictor variables.</p>\n\n<p>There are different orders of polynomial regression:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<center><b>Quadratic - 2nd Order</b></center>\n$$\nYhat = a + b_1 X +b_2 X^2 \n$$\n\n<center><b>Cubic - 3rd Order</b></center>\n$$\nYhat = a + b_1 X +b_2 X^2 +b_3 X^3\\\\\\\\\\\\\\\\\\\\\n$$\n\n<center><b>Higher-Order</b>:</center>\n$$\nY = a + b_1 X +b_2 X^2 +b_3 X^3 ....\\\\\\\\\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>We saw earlier that a linear model did not provide the best fit while using \"highway-mpg\" as the predictor variable. Let's see if we can try fitting a polynomial model to the data instead.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>We will use the following function to plot the data:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def PlotPolly(model, independent_variable, dependent_variabble, Name):\n    x_new = np.linspace(15, 55, 100)\n    y_new = model(x_new)\n\n    plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-')\n    plt.title('Polynomial Fit with Matplotlib for Price ~ Length')\n    ax = plt.gca()\n    ax.set_facecolor((0.898, 0.898, 0.898))\n    fig = plt.gcf()\n    plt.xlabel(Name)\n    plt.ylabel('Price of Cars')\n\n    plt.show()\n    plt.close()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Let's get the variables:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "x = df['highway-mpg']\ny = df['price']",
      "metadata": {
        "trusted": true
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Let's fit the polynomial using the function <b>polyfit</b>, then use the function <b>poly1d</b> to display the polynomial function.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Here we use a polynomial of the 3rd order (cubic) \nf = np.polyfit(x, y, 3)\np = np.poly1d(f)\nprint(p)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 52,
      "outputs": [
        {
          "name": "stdout",
          "text": "        3         2\n-1.557 x + 204.8 x - 8965 x + 1.379e+05\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's plot the function:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "PlotPolly(p, x, y, 'highway-mpg')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x86e82d0>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "np.polyfit(x, y, 3)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 54,
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([-1.55663829e+00,  2.04754306e+02, -8.96543312e+03,  1.37923594e+05])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>We can already see from plotting that this polynomial model performs better than the linear model. This is because the generated polynomial function  \"hits\" more of the data points.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question  #4:</h1>\n<b>Create 11 order polynomial model with the variables x and y from above.</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nnp.polyfit(x, y, 11)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 55,
      "outputs": [
        {
          "execution_count": 55,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([-1.24263622e-08,  4.72185774e-06, -8.02844994e-04,  8.05641836e-02,\n       -5.29742001e+00,  2.39470796e+02, -7.58847246e+03,  1.68447594e+05,\n       -2.56531683e+06,  2.55141164e+07, -1.49107290e+08,  3.87930961e+08])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# Here we use a polynomial of the 11rd order (cubic) \nf1 = np.polyfit(x, y, 11)\np1 = np.poly1d(f1)\nprint(p1)\nPlotPolly(p1,x,y, 'Highway MPG')\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>The analytical expression for Multivariate Polynomial function gets complicated. For example, the expression for a second-order (degree=2) polynomial with two variables is given by:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "$$\nYhat = a + b\\_1 X\\_1 +b\\_2 X\\_2 +b\\_3 X\\_1 X\\_2+b\\_4 X\\_1^2+b\\_5 X\\_2^2\n$$\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can perform a polynomial transform on multiple features. First, we import the module:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import PolynomialFeatures",
      "metadata": {
        "trusted": true
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We create a <b>PolynomialFeatures</b> object of degree 2:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pr=PolynomialFeatures(degree=2)\npr",
      "metadata": {
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "execution_count": 57,
          "output_type": "execute_result",
          "data": {
            "text/plain": "PolynomialFeatures()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "Z_pr=pr.fit_transform(Z)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "In the original data, there are 201 samples and 4 features.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z.shape",
      "metadata": {
        "trusted": true
      },
      "execution_count": 59,
      "outputs": [
        {
          "execution_count": 59,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(201, 4)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "After the transformation, there are 201 samples and 15 features.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z_pr.shape",
      "metadata": {
        "trusted": true
      },
      "execution_count": 60,
      "outputs": [
        {
          "execution_count": 60,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(201, 15)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Pipeline</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Data Pipelines simplify the steps of processing the data. We use the module <b>Pipeline</b> to create a pipeline. We also use <b>StandardScaler</b> as a step in our pipeline.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler",
      "metadata": {
        "trusted": true
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We create the pipeline by creating a list of tuples including the name of the model or estimator and its corresponding constructor.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We input the list as an argument to the pipeline constructor:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pipe=Pipeline(Input)\npipe",
      "metadata": {
        "trusted": true
      },
      "execution_count": 63,
      "outputs": [
        {
          "execution_count": 63,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Pipeline(steps=[('scale', StandardScaler()),\n                ('polynomial', PolynomialFeatures(include_bias=False)),\n                ('model', LinearRegression())])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "First, we convert the data type Z to type float to avoid conversion warnings that may appear as a result of StandardScaler taking float inputs.\n\nThen, we can normalize the data,  perform a transform and fit the model simultaneously.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z = Z.astype(float)\npipe.fit(Z,y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 64,
      "outputs": [
        {
          "execution_count": 64,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Pipeline(steps=[('scale', StandardScaler()),\n                ('polynomial', PolynomialFeatures(include_bias=False)),\n                ('model', LinearRegression())])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Similarly,  we can normalize the data, perform a transform and produce a prediction  simultaneously.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "ypipe=pipe.predict(Z)\nypipe[0:4]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 65,
      "outputs": [
        {
          "execution_count": 65,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([13102.74784201, 13102.74784201, 18225.54572197, 10390.29636555])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question #5:</h1>\n<b>Create a pipeline that standardizes the data, then produce a prediction using a linear regression model using the features Z and target y.</b>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \npipe = Pipeline([('scale', StandardScaler()), ('model', LinearRegression())])\npipe.fit(Z, y)\nyHat = pipe.predict(Z)\nyHat[0:4]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 71,
      "outputs": [
        {
          "execution_count": 71,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([13699.11161184, 13699.11161184, 19051.65470233, 10620.36193015])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nInput=[('scale',StandardScaler()),('model',LinearRegression())]\n\npipe=Pipeline(Input)\n\npipe.fit(Z,y)\n\nypipe=pipe.predict(Z)\nypipe[0:10]\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>4. Measures for In-Sample Evaluation</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>When evaluating our models, not only do we want to visualize the results, but we also want a quantitative measure to determine how accurate the model is.</p>\n\n<p>Two very important measures that are often used in Statistics to determine the accuracy of a model are:</p>\n<ul>\n    <li><b>R^2 / R-squared</b></li>\n    <li><b>Mean Squared Error (MSE)</b></li>\n</ul>\n\n<b>R-squared</b>\n\n<p>R squared, also known as the coefficient of determination, is a measure to indicate how close the data is to the fitted regression line.</p>\n\n<p>The value of the R-squared is the percentage of variation of the response variable (y) that is explained by a linear model.</p>\n\n<b>Mean Squared Error (MSE)</b>\n\n<p>The Mean Squared Error measures the average of the squares of errors. That is, the difference between actual value (y) and the estimated value (ŷ).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Model 1: Simple Linear Regression</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the R^2:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#highway_mpg_fit\nlm.fit(X, Y)\n# Find the R^2\nprint('The R-square is: ', lm.score(X, Y))",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 72,
      "outputs": [
        {
          "name": "stdout",
          "text": "The R-square is:  0.4965911884339176\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can say that \\~49.659% of the variation of the price is explained by this simple linear model \"horsepower_fit\".\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the MSE:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can predict the output i.e., \"yhat\" using the predict method, where X is the input variable:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Yhat=lm.predict(X)\nprint('The output of the first four predicted value is: ', Yhat[0:4])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 73,
      "outputs": [
        {
          "name": "stdout",
          "text": "The output of the first four predicted value is:  [16236.50464347 16236.50464347 17058.23802179 13771.3045085 ]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's import the function <b>mean_squared_error</b> from the module <b>metrics</b>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import mean_squared_error",
      "metadata": {
        "trusted": true
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can compare the predicted results with the actual results:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mse = mean_squared_error(df['price'], Yhat)\nprint('The mean square error of price and predicted value is: ', mse)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 75,
      "outputs": [
        {
          "name": "stdout",
          "text": "The mean square error of price and predicted value is:  31635042.944639888\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Model 2: Multiple Linear Regression</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the R^2:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# fit the model \nlm.fit(Z, df['price'])\n# Find the R^2\nprint('The R-square is: ', lm.score(Z, df['price']))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 76,
      "outputs": [
        {
          "name": "stdout",
          "text": "The R-square is:  0.8093562806577457\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can say that \\~80.896 % of the variation of price is explained by this multiple linear regression \"multi_fit\".\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the MSE.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We produce a prediction:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_predict_multifit = lm.predict(Z)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We compare the predicted results with the actual results:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print('The mean square error of price and predicted value using multifit is: ', \\\n      mean_squared_error(df['price'], Y_predict_multifit))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 78,
      "outputs": [
        {
          "name": "stdout",
          "text": "The mean square error of price and predicted value using multifit is:  11980366.87072649\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Model 3: Polynomial Fit</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the R^2.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let’s import the function <b>r2\\_score</b> from the module <b>metrics</b> as we are using a different function.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import r2_score",
      "metadata": {
        "trusted": true
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We apply the function to get the value of R^2:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "r_squared = r2_score(y, p(x))\nprint('The R-square value is: ', r_squared)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 81,
      "outputs": [
        {
          "name": "stdout",
          "text": "The R-square value is:  0.674194666390652\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can say that \\~67.419 % of the variation of price is explained by this polynomial fit.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>MSE</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can also calculate the MSE:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean_squared_error(df['price'], p(x))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 82,
      "outputs": [
        {
          "execution_count": 82,
          "output_type": "execute_result",
          "data": {
            "text/plain": "20474146.426361218"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2>5. Prediction and Decision Making</h2>\n<h3>Prediction</h3>\n\n<p>In the previous section, we trained the model using the method <b>fit</b>. Now we will use the method <b>predict</b> to produce a prediction. Lets import <b>pyplot</b> for plotting; we will also be using some functions from numpy.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport numpy as np\n\n%matplotlib inline ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Create a new input:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "new_input=np.arange(1, 100, 1).reshape(-1, 1)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Fit the model:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lm.fit(X, Y)\nlm",
      "metadata": {
        "trusted": true
      },
      "execution_count": 89,
      "outputs": [
        {
          "execution_count": 89,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Produce a prediction:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=lm.predict(new_input)\nyhat[0:5]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 90,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 90,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([37601.57247984, 36779.83910151, 35958.10572319, 35136.37234487,\n       34314.63896655])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the data:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "plt.plot(new_input, yhat)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x89f29d0>",
            "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": "<h3>Decision Making: Determining a Good Model Fit</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Now that we have visualized the different models, and generated the R-squared and MSE values for the fits, how do we determine a good model fit?\n<ul>\n    <li><i>What is a good R-squared value?</i></li>\n</ul>\n</p>\n\n<p>When comparing models, <b>the model with the higher R-squared value is a better fit</b> for the data.\n<ul>\n    <li><i>What is a good MSE?</i></li>\n</ul>\n</p>\n\n<p>When comparing models, <b>the model with the smallest MSE value is a better fit</b> for the data.</p>\n\n<h4>Let's take a look at the values for the different models.</h4>\n<p>Simple Linear Regression: Using Highway-mpg as a Predictor Variable of Price.\n<ul>\n    <li>R-squared: 0.49659118843391759</li>\n    <li>MSE: 3.16 x10^7</li>\n</ul>\n</p>\n\n<p>Multiple Linear Regression: Using Horsepower, Curb-weight, Engine-size, and Highway-mpg as Predictor Variables of Price.\n<ul>\n    <li>R-squared: 0.80896354913783497</li>\n    <li>MSE: 1.2 x10^7</li>\n</ul>\n</p>\n\n<p>Polynomial Fit: Using Highway-mpg as a Predictor Variable of Price.\n<ul>\n    <li>R-squared: 0.6741946663906514</li>\n    <li>MSE: 2.05 x 10^7</li>\n</ul>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Simple Linear Regression Model (SLR) vs Multiple Linear Regression Model (MLR)</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Usually, the more variables you have, the better your model is at predicting, but this is not always true. Sometimes you may not have enough data, you may run into numerical problems, or many of the variables may not be useful and even act as noise. As a result, you should always check the MSE and R^2.</p>\n\n<p>In order to compare the results of the MLR vs SLR models, we look at a combination of both the R-squared and MSE to make the best conclusion about the fit of the model.\n<ul>\n    <li><b>MSE</b>: The MSE of SLR is  3.16x10^7  while MLR has an MSE of 1.2 x10^7.  The MSE of MLR is much smaller.</li>\n    <li><b>R-squared</b>: In this case, we can also see that there is a big difference between the R-squared of the SLR and the R-squared of the MLR. The R-squared for the SLR (~0.497) is very small compared to the R-squared for the MLR (~0.809).</li>\n</ul>\n</p>\n\nThis R-squared in combination with the MSE show that MLR seems like the better model fit in this case compared to SLR.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Simple Linear Model (SLR) vs. Polynomial Fit</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<ul>\n    <li><b>MSE</b>: We can see that Polynomial Fit brought down the MSE, since this MSE is smaller than the one from the SLR.</li> \n    <li><b>R-squared</b>: The R-squared for the Polynomial Fit is larger than the R-squared for the SLR, so the Polynomial Fit also brought up the R-squared quite a bit.</li>\n</ul>\n<p>Since the Polynomial Fit resulted in a lower MSE and a higher R-squared, we can conclude that this was a better fit model than the simple linear regression for predicting \"price\" with \"highway-mpg\" as a predictor variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Multiple Linear Regression (MLR) vs. Polynomial Fit</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<ul>\n    <li><b>MSE</b>: The MSE for the MLR is smaller than the MSE for the Polynomial Fit.</li>\n    <li><b>R-squared</b>: The R-squared for the MLR is also much larger than for the Polynomial Fit.</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Conclusion</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Comparing these three models, we conclude that <b>the MLR model is the best model</b> to be able to predict price from our dataset. This result makes sense since we have 27 variables in total and we know that more than one of those variables are potential predictors of the final car price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Mahdi Noorian PhD</a>\n\nBahare Talayian\n\nEric Xiao\n\nSteven Dong\n\nParizad\n\nHima Vasudevan\n\n<a href=\"https://www.linkedin.com/in/fiorellawever/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Fiorella Wenver</a>\n\n<a href=\"https:// https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" >Yi Yao</a>.\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                            |\n| ----------------- | ------- | ---------- | --------------------------------------------- |\n| 2020-10-30        | 2.2     | Lakshmi    | Changed url of csv                            |\n| 2020-09-09        | 2.1     | Lakshmi    | Fixes made in Polynomial Regression Equations |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab            |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}