practice_project (1).ipynb

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  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
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    "language_info": {
      "codemirror_mode": {
        "name": "python",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
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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=\"300\" alt=\"Skills Network Logo\">\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **Practice Project: Insurance Cost Analysis**\n\nEstimated time needed: **75** minutes\n\nIn this project, you have to perform analytics operations on an insurance database that uses the below mentioned parameters.\n\n| Parameter |Description| Content type |\n|---|----|---|\n|age| Age in years| integer |\n|gender| Male or Female|integer (1 or 2)|\n| bmi | Body mass index | float |\n|no_of_children| Number of children | integer|\n|smoker| Whether smoker or not | integer (0 or 1)|\n|region| Which US region - NW, NE, SW, SE | integer (1,2,3 or 4 respectively)| \n|charges| Annual Insurance charges in USD | float|\n\n## Objectives \nIn this project, you will:\n - Load the data as a `pandas` dataframe\n - Clean the data, taking care of the blank entries\n - Run exploratory data analysis (EDA) and identify the attributes that most affect the `charges`\n - Develop single variable and multi variable Linear Regression models for predicting the `charges`\n - Use Ridge regression to refine the performance of Linear regression models. \n \n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Setup\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "For this lab, we will be using the following libraries:\n* `skillsnetwork` to download the data\n*   [`pandas`](https://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for managing the data.\n*   [`numpy`](https://numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for mathematical operations.\n*   [`sklearn`](https://scikit-learn.org/stable/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for machine learning and machine-learning-pipeline related functions.\n*   [`seaborn`](https://seaborn.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for visualizing the data.\n*   [`matplotlib`](https://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for additional plotting tools.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The following required libraries are __not__ pre-installed in the Skills Network Labs environment. __You will need to run the following cell__ to install them:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install('seaborn')",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": "### Importing Required Libraries\n\n_We recommend you import all required libraries in one place (here):_\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-2-114b4bb00877>:1: DeprecationWarning: \nPyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\nbut was not found to be installed on your system.\nIf this would cause problems for you,\nplease provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n        \n  import pandas as pd\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.model_selection import cross_val_score, train_test_split\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Download the dataset to this lab environment\n\nRun the cell below to load the dataset to this lab environment.\n",
      "metadata": {}
    },
    {
      "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
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "source": "filepath = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-Coursera/medical_insurance_dataset.csv'",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "await download(filepath, \"insurance.csv\")\nfile_name=\"insurance.csv\"",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(file_name,header=None)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": "> Note: This version of the lab is working on JupyterLite, which requires the dataset to be downloaded to the interface.While working on the downloaded version of this notebook on their local machines(Jupyter Anaconda), the learners can simply **skip the steps above,** and simply use the URL directly in the `pandas.read_csv()` function. You can uncomment and run the statements in the cell below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#filepath = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-Coursera/medical_insurance_dataset.csv'\n#df = pd.read_csv(filepath, header=None)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": "# Task 1 : Import the dataset\n\nImport the dataset into a `pandas` dataframe. Note that there are currently no headers in the CSV file. \n\nPrint the first 10 rows of the dataframe to confirm successful loading.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.head(10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    0  1       2  3  4  5            6\n0  19  1  27.900  0  1  3  16884.92400\n1  18  2  33.770  1  0  4   1725.55230\n2  28  2  33.000  3  0  4   4449.46200\n3  33  2  22.705  0  0  1  21984.47061\n4  32  2  28.880  0  0  1   3866.85520\n5  31  1  25.740  0  ?  4   3756.62160\n6  46  1  33.440  1  0  4   8240.58960\n7  37  1  27.740  3  0  1   7281.50560\n8  37  2  29.830  2  0  2   6406.41070\n9  60  1  25.840  0  0  1  28923.13692",
            "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>1</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>16884.92400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>2</td>\n      <td>33.770</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1725.55230</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>2</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>4449.46200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>2</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21984.47061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>2</td>\n      <td>28.880</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3866.85520</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>31</td>\n      <td>1</td>\n      <td>25.740</td>\n      <td>0</td>\n      <td>?</td>\n      <td>4</td>\n      <td>3756.62160</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>46</td>\n      <td>1</td>\n      <td>33.440</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>8240.58960</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>37</td>\n      <td>1</td>\n      <td>27.740</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7281.50560</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>37</td>\n      <td>2</td>\n      <td>29.830</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>6406.41070</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>60</td>\n      <td>1</td>\n      <td>25.840</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>28923.13692</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\ndf = pd.read_csv(path, header=None)\nprint(df.head(10))\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Add the headers to the dataframe, as mentioned in the project scenario. \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "headers=['Age','Gender','BMI','No_of_Children','Smoker','Region','Charges']\ndf.columns=headers\ndf.head(10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  Age  Gender     BMI  No_of_Children Smoker  Region      Charges\n0  19       1  27.900               0      1       3  16884.92400\n1  18       2  33.770               1      0       4   1725.55230\n2  28       2  33.000               3      0       4   4449.46200\n3  33       2  22.705               0      0       1  21984.47061\n4  32       2  28.880               0      0       1   3866.85520\n5  31       1  25.740               0      ?       4   3756.62160\n6  46       1  33.440               1      0       4   8240.58960\n7  37       1  27.740               3      0       1   7281.50560\n8  37       2  29.830               2      0       2   6406.41070\n9  60       1  25.840               0      0       1  28923.13692",
            "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>Age</th>\n      <th>Gender</th>\n      <th>BMI</th>\n      <th>No_of_Children</th>\n      <th>Smoker</th>\n      <th>Region</th>\n      <th>Charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>1</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>16884.92400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>2</td>\n      <td>33.770</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1725.55230</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>2</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>4449.46200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>2</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21984.47061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>2</td>\n      <td>28.880</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3866.85520</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>31</td>\n      <td>1</td>\n      <td>25.740</td>\n      <td>0</td>\n      <td>?</td>\n      <td>4</td>\n      <td>3756.62160</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>46</td>\n      <td>1</td>\n      <td>33.440</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>8240.58960</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>37</td>\n      <td>1</td>\n      <td>27.740</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7281.50560</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>37</td>\n      <td>2</td>\n      <td>29.830</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>6406.41070</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>60</td>\n      <td>1</td>\n      <td>25.840</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>28923.13692</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nheaders = [\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\", \"charges\"]\ndf.columns = headers\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now, replace the '?' entries with 'NaN' values.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import numpy as np\ndf.replace('?',np.nan,inplace=True)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  Age  Gender     BMI  No_of_Children Smoker  Region      Charges\n0  19       1  27.900               0      1       3  16884.92400\n1  18       2  33.770               1      0       4   1725.55230\n2  28       2  33.000               3      0       4   4449.46200\n3  33       2  22.705               0      0       1  21984.47061\n4  32       2  28.880               0      0       1   3866.85520",
            "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>Age</th>\n      <th>Gender</th>\n      <th>BMI</th>\n      <th>No_of_Children</th>\n      <th>Smoker</th>\n      <th>Region</th>\n      <th>Charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>1</td>\n      <td>27.900</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>16884.92400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>2</td>\n      <td>33.770</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1725.55230</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>2</td>\n      <td>33.000</td>\n      <td>3</td>\n      <td>0</td>\n      <td>4</td>\n      <td>4449.46200</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>2</td>\n      <td>22.705</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>21984.47061</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>2</td>\n      <td>28.880</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3866.85520</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\ndf.replace('?', np.nan, inplace = True)\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Task 2 : Data Wrangling\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use `dataframe.info()` to identify the columns that have some 'Null' (or NaN) information.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.info()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2772 entries, 0 to 2771\nData columns (total 7 columns):\n #   Column          Non-Null Count  Dtype  \n---  ------          --------------  -----  \n 0   Age             2768 non-null   object \n 1   Gender          2772 non-null   int64  \n 2   BMI             2772 non-null   float64\n 3   No_of_Children  2772 non-null   int64  \n 4   Smoker          2765 non-null   object \n 5   Region          2772 non-null   int64  \n 6   Charges         2772 non-null   float64\ndtypes: float64(2), int64(3), object(2)\nmemory usage: 130.0+ KB\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nprint(df.info())\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Handle missing data:\n\n- For continuous attributes (e.g., age), replace missing values with the mean.\n- For categorical attributes (e.g., smoker), replace missing values with the most frequent value.\n- Update the data types of the respective columns.\n- Verify the update using `df.info()`.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#continuous \navg_age=df['Age'].astype(float).mean(axis=0)\ndf['Age'].replace(np.nan,avg_age,inplace=True)\navg_bmi=df['BMI'].astype(float).mean(axis=0)\ndf['BMI'].replace(np.nan,avg_bmi,inplace=True)\navg_No_of_Children=df['No_of_Children'].astype(float).mean(axis=0)\ndf['No_of_Children'].replace(np.nan,avg_No_of_Children,inplace=True)\navg_Charges=df['Charges'].astype(float).mean(axis=0)\ndf['Charges'].replace(np.nan,avg_Charges,inplace=True)\n#Categorical\ndf['Gender'].replace(np.nan,df['Gender'].value_counts().idxmax(),inplace=True)\ndf['Smoker'].replace(np.nan,df['Smoker'].value_counts().idxmax(),inplace=True)\ndf['Region'].replace(np.nan,df['Region'].value_counts().idxmax(),inplace=True)\n#update data types\ndf[['Age','Gender','No_of_Children','Smoker','Region']]=df[['Age','Gender','No_of_Children','Smoker','Region']].astype('int')\nprint(df.info())",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-12-cfa45c4ef598>:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['Age'].replace(np.nan,avg_age,inplace=True)\n<ipython-input-12-cfa45c4ef598>:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['BMI'].replace(np.nan,avg_bmi,inplace=True)\n<ipython-input-12-cfa45c4ef598>:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['No_of_Children'].replace(np.nan,avg_No_of_Children,inplace=True)\n<ipython-input-12-cfa45c4ef598>:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['Charges'].replace(np.nan,avg_Charges,inplace=True)\n<ipython-input-12-cfa45c4ef598>:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['Gender'].replace(np.nan,df['Gender'].value_counts().idxmax(),inplace=True)\n<ipython-input-12-cfa45c4ef598>:12: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['Smoker'].replace(np.nan,df['Smoker'].value_counts().idxmax(),inplace=True)\n<ipython-input-12-cfa45c4ef598>:13: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\nThe behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n\nFor example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n\n\n  df['Region'].replace(np.nan,df['Region'].value_counts().idxmax(),inplace=True)\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2772 entries, 0 to 2771\nData columns (total 7 columns):\n #   Column          Non-Null Count  Dtype  \n---  ------          --------------  -----  \n 0   Age             2772 non-null   int32  \n 1   Gender          2772 non-null   int32  \n 2   BMI             2772 non-null   float64\n 3   No_of_Children  2772 non-null   int32  \n 4   Smoker          2772 non-null   int32  \n 5   Region          2772 non-null   int32  \n 6   Charges         2772 non-null   float64\ndtypes: float64(2), int32(5)\nmemory usage: 97.5 KB\nNone\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 12
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# smoker is a categorical attribute, replace with most frequent entry\nis_smoker = df['smoker'].value_counts().idxmax()\ndf[\"smoker\"].replace(np.nan, is_smoker, inplace=True)\n\n# age is a continuous variable, replace with mean age\nmean_age = df['age'].astype('float').mean(axis=0)\ndf[\"age\"].replace(np.nan, mean_age, inplace=True)\n\n# Update data types\ndf[[\"age\",\"smoker\"]] = df[[\"age\",\"smoker\"]].astype(\"int\")\n\nprint(df.info())\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Also note, that the `charges` column has values which are more than 2 decimal places long. Update the `charges` column such that all values are rounded to nearest 2 decimal places. Verify conversion by printing the first 5 values of the updated dataframe.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['Charges']=np.round(df['Charges'],2)\nprint(df.head(5))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "   Age  Gender     BMI  No_of_Children  Smoker  Region   Charges\n0   19       1  27.900               0       1       3  16884.92\n1   18       2  33.770               1       0       4   1725.55\n2   28       2  33.000               3       0       4   4449.46\n3   33       2  22.705               0       0       1  21984.47\n4   32       2  28.880               0       0       1   3866.86\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\ndf[[\"charges\"]] = np.round(df[[\"charges\"]],2)\nprint(df.head())\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Task 3 : Exploratory Data Analysis (EDA)\n\nImplement the regression plot for `charges` with respect to `bmi`. \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "%matplotlib inline\nsns.regplot(x='BMI',y='Charges',data=df)\nplt.ylim(0,)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 66902.85800000001)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nsns.regplot(x=\"bmi\", y=\"charges\", data=df, line_kws={\"color\": \"red\"})\nplt.ylim(0,)\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Implement the box plot for `charges` with respect to `smoker`.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x='Smoker',y='Charges',data=df)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Smoker', ylabel='Charges'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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ZeuSRRwI+2NraWs2ePVtr166V0+nU5MmT9dBDDykuLs6sqaqq0qxZs/T6668rLi5O2dnZWrZsmcLDg+YjAnAa8Ee75Y89w+42ABxDUEwEf/fdd/Xoo49qyJAhAdvz8/NVUFCgwsJClZeXKzY2VpmZmWpsbDRr5syZo7Vr16q4uFibNm1SdXW1Jk2aFPA6U6dO1bZt21RaWqqSkhJt3rxZOTk55nhbW5vGjRun5uZmbdmyRatWrdLKlSu1ZMmSzn3jAACgy7A9NB04cEBTp07VY489ph49epjbDcPQgw8+qEWLFmnChAkaMmSInnrqKVVXV+vFF1+UdOhw9uOPP64//OEP+tnPfqZhw4bpySef1JYtW/T2229Lknbs2KH169frP//zP5Wenq5Ro0bpj3/8o1avXq3q6mpJ0muvvabt27fr6aef1nnnnafLL79cd911l5YvX67m5ubj9t7U1KT6+vqABwAACE22h6bc3FyNGzdOGRkZAdt3794tr9cbsN3tdis9PV1lZWWSpIqKCrW0tATUDBw4UKmpqWZNWVmZ4uPjNXz4cLMmIyNDTqdT5eXlZs3gwYMDTullZmaqvr5e27ZtO27vy5Ytk9vtNh8pKSkn8UkAAIBgZmtoWr16td5//30tW7bsqDGv1ytJR036SkxMNMe8Xq8iIyMVHx//vTW9evUKGA8PD1dCQkJAzbH2c2Qfx7Jw4UL5fD7zsXfv3v/tLQMAgC7KtlnOe/fu1S233KLS0lJFR0fb1cZJiYqKUlRUlN1tAACAU8C2I00VFRXat2+fLrjgAoWHhys8PFybNm1SQUGBwsPDzSM9NTU1Ab9XU1OjpKQkSVJSUpKam5tVV1f3vTX79u0LGG9tbVVtbW1AzbH2c3gMAADAttA0evRobd26VR9++KH5GD58uKZOnaoPP/xQZ599tpKSkrRhwwbzd+rr61VeXi6PxyNJGjZsmCIiIgJqKisrVVVVZdZ4PB7V1dWpoqLCrNm4caP8fr/S09PNmq1btwaEq9LSUrlcLqWlpXXq5wAAALoG207Pde/eXeeee27AttjYWPXs2dPcfuutt+ruu+9W//791a9fPy1evFjJycmaOHGipEMTw2fMmKG5c+cqISFBLpdLs2fPlsfj0YgRIyRJgwYN0tixYzVz5kwVFhaqpaVFeXl5ysrKUnJysiRpzJgxSktL07Rp05Sfny+v16tFixYpNzeX028AAEBSEC1ueSwLFixQQ0ODcnJyVFdXp1GjRmn9+vUBc6AeeOABc8HKIxe3PNIzzzyjvLw8jR492qwtKCgwx8PCwlRSUqJZs2bJ4/EoNjZW2dnZuvPOO0/ZewUAAMGNe891IO49B6C9du7cqZycHDWkXcWK4MARnA3fKHb7yyoqKtKAAQM6ZR9Wv79tX6cJAACgKyA0AQAAWEBoAgAAsIDQBAAAYEFQXz0HBKvm5ma99NJLqq6uVnJysiZMmKDIyEi72wIAdCJCE3CCCgsLVVxcrLa2toBtU6ZM0U033WRjZwCAzkRoAk5AYWGhVq9erR49emjo0KGKiYnRwYMH9dFHH2n16tWSRHACgBDVrtB08OBBGYahbt26SZL27NmjNWvWKC0tTWPGjOnQBoFg0dzcrOLiYkVFRamurk5vvPGGOeZwOBQVFaXi4mLdcMMNnKoDgBDUrongEyZM0FNPPSVJqqurU3p6uu6//35NmDBBK1as6NAGgWDx0ksvqa2tTU1NTYqPj9f8+fP117/+VfPnz1d8fLyamprU1taml156ye5WAQCdoF2h6f3339fFF18sSfrLX/6ixMRE7dmzR0899VTA7UmAULJ3715Jh+55WFxcrPHjx6tnz54aP368iouL5Xa7A+oAAKGlXaHpn//8p7p37y5Jeu211zRp0iQ5nU6NGDFCe/bs6dAGgWDx7bffSpLS09MVHh54Zjs8PFwXXnhhQB0AILS0KzT96Ec/0osvvqi9e/fq1VdfNecx7du3j3uuIWSdccah+4GVl5ertbU1YKy1tVXvvPNOQB0AILS0KzQtWbJE8+fP11lnnaULL7xQHo9H0qGjTueff36HNggEiz59+kiSfD6fpkyZorVr1+qbb77R2rVrNWXKFPl8voA6AEBoadfVc9dcc41GjRqlr776SkOHDjW3jx49WldffXWHNQcEkwkTJqiwsFDh4eHy+Xy6//77zTGn06moqCi1trZqwoQJNnYJAOgs7b6NSlJSkrp3767S0lIdPHhQkvSTn/xEAwcO7LDmgGASGRmpKVOmqKmpSX6/P2DM7/erqalJU6ZMYbkBAAhR7QpN3377rUaPHq0BAwboiiuu0FdffSVJmjFjhubNm9ehDQIAAASDdoWmOXPmKCIiQlVVVeYCl5J03XXXaf369R3WHBBMDi9u2aNHD73yyivKzc3V1VdfrdzcXL3yyivq0aOHiouL1dzcbHerAIBO0K45Ta+99ppeffXVoya89u/fnyUHELIOL245Y8YMdevWTVOmTAkYv+GGG3T//ffrpZdeOmoMAND1tetIU0NDQ8ARpsNqa2sVFRV10k0Bwai6ulqSzKtF/9Xh7YfrAAChpV2h6eKLLzZvoyIduu+W3+9Xfn6+Lr300g5rDggmycnJkqSysrJjjh/efrgOABBa2nV6Lj8/X6NHj9Z7772n5uZmLViwQNu2bVNtba3eeuutju4RCAqHlxx4/PHHNXbs2IBVwVtbW/XEE08oLCyMJQdwUpwH6+xuAQgqwfQ30a7QdO6552rnzp16+OGH1b17dx04cECTJk1Sbm6uevfu3dE9AkHh8JIDq1ev1pQpU3TDDTfI4/GorKxMTzzxhL777jtlZWWx5ABOSszuzXa3AOA42hWapEM3Lb3jjjs6shcg6N10002SpOeff/6oxS2zsrLMcaC9Dvb7P/LHxNvdBhA0nAfrguYfE+0KTR9//PExtzscDkVHRys1NZUJ4QhpDodDhmHY3QZCkD8mXv5Y7l8IBKN2habzzjtPDodDkswvjsPPJSkiIkLXXXedHn30UUVHR3dAm0BwKCws1OrVq9WjRw/NmDHDPD33+OOPa/Xq1ZLE0SYACFHtunpuzZo16t+/v4qKivTRRx/po48+UlFRkc455xw9++yzevzxx7Vx40YtWrSoo/sFbHPk4pbFxcUaP368evbsqfHjxwdsZ3FLAAhN7TrSdM899+ihhx5SZmamuW3w4MHq06ePFi9erHfeeUexsbGaN2+efv/733dYs4Cdjlzc8sgr5yQpPDycxS0BIMS160jT1q1b1bdv36O29+3bV1u3bpV06BTe4XvSAaGAxS0B4PTWrtA0cOBA3XvvvQGnIVpaWnTvvfdq4MCBkqQvv/xSiYmJHdMlEARY3BIATm/tCk3Lly9XSUmJ+vTpo4yMDGVkZKhPnz4qKSnRihUrJElffPGFfv3rX3dos4CdJkyYoLCwMD3++ONqbW0NGGNxSwAIfe2a03TRRRdp9+7deuaZZ7Rz505J0pQpU/SLX/xC3bt3lyRNmzat47oEgsCRi1tOnjxZZ5xxhpqbmxUZGalvvvlGPp+PxS0BIISdcGhqaWnRwIEDVVJSwqXVOO3cdNNNeu2111RbWyufzxcwlpCQwN8EAISwEz49FxERocbGxs7oBQh6N910k2pra+VwOHTmmWcqOTlZZ555phwOh2prawlNABDC2jWnKTc3V7/73e+OmtcBhLIDBw7o008/lXRoUdevv/5a1dXV+vrrr81FXj/99FMdOHDAzjYBAJ2kXXOa3n33XW3YsEGvvfaaBg8erNjY2IDxF154oUOaA4LJsmXLzJ+PtSL4d999Z9bdc889drUJAOgk7QpN8fHxmjx5ckf3AgS1w+svde/eXcXFxeYCl+PHj9fYsWN19dVXa//+/azTBAAhql2h6cknn+zoPoCgd/gUXEpKyjFXBO/Tp4927NjBjXwBIES1a04TcDo699xzJUk7duw46mKIxsZGc77T4ToAQGhp15EmSfrLX/6iP//5z6qqqjrqBqXvv//+STcGBJvDtw4yDEOXX365MjIyNGXKFBUXF+tvf/ubeYTpWLcYAgB0fe060lRQUKDp06crMTFRH3zwgS688EL17NlTX3zxhS6//PKO7hEICodXBHc6nTIMQ6WlpcrJyVFpaakMw5DT6WRFcAAIYe0KTY888oiKior0xz/+UZGRkVqwYIFKS0t18803H7XgHxAqDq8I7vf71b17d0VERMjhcCgiIkLdu3eX3+/XlClTWBEcAEJUu0JTVVWVLrroIklSTEyM9u/fL+nQrVOee+65jusOCDI33XSToqOjtX//frW0tMgwDLW0tGj//v2Kjo5mcUsACGHtCk1JSUmqra2VJKWmpurtt9+WJO3evfuErhxasWKFhgwZIpfLJZfLJY/Ho3Xr1pnjhmFoyZIl6t27t2JiYpSRkaFdu3YFvEZjY6Nyc3PVs2dPxcXFafLkyaqpqQmoqa2t1dSpU+VyuRQfH68ZM2YctQBhVVWVxo0bp27duqlXr1667bbbWLwTR5k0adJxV8RvbGzUpEmTTnFHAIBTpV2h6Wc/+5lefvllSdL06dM1Z84cXXbZZbruuut09dVXW36dPn366N5771VFRYXee+89/exnP9OECRO0bds2SVJ+fr4KCgpUWFio8vJyxcbGKjMzM+BLa86cOVq7dq2Ki4u1adMmVVdXH/XFNXXqVG3btk2lpaUqKSnR5s2blZOTY463tbVp3Lhxam5u1pYtW7Rq1SqtXLlSS5Ysac/HgxBVW1tr/mMhPj5e8+fP11//+lfNnz9f8fHxR9UAAEKLw2jHojJ+v19+v99cq2b16tXasmWL+vfvrxtvvPGk5nQkJCTovvvu0w033KDk5GTNmzdP8+fPlyT5fD4lJiZq5cqVysrKks/n05lnnqlnn31W11xzjaRDt7EYNGiQysrKNGLECO3YsUNpaWl69913NXz4cEnS+vXrdcUVV+gf//iHkpOTtW7dOo0fP17V1dVKTEyUJBUWFur222/X119/bfn91NfXy+12y+fzyeVytfszQHDKzs7Wnj17FBYWpldffTVgrabW1lZlZmaqra1Nffv21apVq2zsFF3Rzp07lZOTo4a0q+SPPcPudoCg4Wz4RrHbX1ZRUZEGDBjQKfuw+v3driNNTqcz4AsjKytLBQUFmj17drsDU1tbm1avXq2GhgZ5PB7t3r1bXq9XGRkZZo3b7VZ6errKysokSRUVFWppaQmoGThwoFJTU82asrIyxcfHm4FJkjIyMuR0OlVeXm7WDB482AxMkpSZman6+nrzqNexNDU1qb6+PuCB0PXVV19JOnSKzu/3q7i4WA899JCKi4vl9/vNq+YO1wEAQku712mqq6vTO++8o3379snv9weMXX/99ZZfZ+vWrfJ4PGpsbFRcXJzWrFmjtLQ0bdmyRZICgszh516vV5Lk9XoVGRlpnho5Xk2vXr0CxsPDw5WQkBBQc6z9HB47nmXLluk3v/mN5feKri0qKkrNzc3629/+phdeeEFtbW3mWGFhoeLi4sw6AEDoaVdoWrt2raZOnaoDBw7I5XLJ4XCYYw6H44RC0znnnKMPP/xQPp9Pf/nLX5Sdna1Nmza1p61TbuHChZo7d675vL6+XikpKTZ2hM40ZcoUPfHEE/ruu+/kdrt1/vnnKyYmRgcPHtQHH3xgLrcxZcoUmzsFAHSGdp2emzdvnm644QYdOHBAdXV1+u6778zHiU6CjYyM1I9+9CMNGzZMy5Yt09ChQ/XQQw8pKSlJko66Eq6mpsYcS0pKUnNzs+rq6r63Zt++fQHjra2tqq2tDag51n4Ojx1PVFSUeeXf4QdC15EXGPh8Pr3xxhtat26d3njjjYD1ybiCDgBCU7tC05dffqmbb75Z3bp16+h+5Pf71dTUpH79+ikpKUkbNmwwx+rr61VeXi6PxyNJGjZsmCIiIgJqKisrVVVVZdZ4PB7V1dWpoqLCrNm4caP8fr/S09PNmq1btwaEq9LSUrlcLqWlpXX4e0TXdORyGB1RBwDoWtp1ei4zM1Pvvfeezj777JPa+cKFC3X55ZcrNTVV+/fv17PPPqs33nhDr776qhwOh2699Vbdfffd6t+/v/r166fFixcrOTlZEydOlHRoYviMGTM0d+5cJSQkyOVyafbs2fJ4PBoxYoQkadCgQRo7dqxmzpypwsJCtbS0KC8vT1lZWUpOTpYkjRkzRmlpaZo2bZry8/Pl9Xq1aNEi5ebmMj8Fpr1793ZoHQCga7Ecmg6vyyRJ48aN02233abt27dr8ODBioiICKi96qqrLL3mvn37dP311+urr76S2+3WkCFD9Oqrr+qyyy6TJC1YsEANDQ3KyclRXV2dRo0apfXr1ys6Otp8jQceeEBOp1OTJ09WU1OTMjMz9cgjjwTs55lnnlFeXp5Gjx5t1hYUFJjjYWFhKikp0axZs+TxeBQbG6vs7GzdeeedVj8enAa+/fbbDq0DAHQtltdpcjqtnclzOBwBVxWdTlinKbTl5+frlVdeMZ+fc845Sk5OVnV1tSorK83tV1xxhRYsWGBHi+jCWKcJOLZgWqfJ8pGmf11WADjdbN26NeB5ZWVlQFg6Xh0AIDSc0ETwjRs3Ki0t7ZiLOPp8Pv34xz/W3//+9w5rDggm33zzTYfWAQC6lhMKTQ8++KBmzpx5zENXbrdbN954o/7whz90WHNAMIuIiFBYWNhRc/oAAKHphELTRx99pLFjxx53fMyYMQGX9gOh5IwzAueZtLS0qK2tTS0tLd9bBwAIDScUmmpqar73X9Xh4eH6+uuvT7opIBgdvk1KR9UBALqWEwpNP/jBD/TJJ58cd/zjjz9W7969T7opIBhZvNDUch0AoGs5odB0xRVXaPHixWpsbDxq7ODBg1q6dKnGjx/fYc0BweSrr77q0DoAQNdyQiuCL1q0SC+88IIGDBigvLw8nXPOOZKkTz/9VMuXL1dbW5vuuOOOTmkUsNs///lP8+e4uDj5/X41NzcrMjJSTqdTBw4cOKoOABA6Tig0JSYmasuWLZo1a5YWLlxonoZwOBzKzMzU8uXLlZiY2CmNAnZzOBzmz4cDknToBtDHqwMAhI4Tvvdc37599corr+i7777TZ599JsMw1L9/f/Xo0aMz+gOCxtlnn61PP/3UUh0AIPS064a9ktSjRw/95Cc/6chegKA2YsQIS6Hp8M2iAQCh5YQmggOnMyuB6UTqAABdC6EJsGj79u0dWgcA6FoITYBFR07+7og6AEDXQmgCAACwgNAEWBQWFtahdQCAroXQBFjUvXv3Dq0DAHQthCbAovBwayt0WK0DAHQthCbAIqsrfbMiOACEJkITYNHh2wZ1VB0AoGshNAEWWb1VELcUAoDQRGgCLKqpqenQOgBA10JoAixqaGjo0DoAQNdCaAIsYk4TAJzeCE2ARfHx8R1aBwDoWghNgEWJiYkdWgcA6FoITYBFHo+nQ+sAAF0LoQmwyO/3d2gdAKBrITQBFpWUlHRoHQCgayE0ARbV1tZKkiIiIo45fnj74ToAQGjhzqKAReHh4WptbVVLS4vcbrfOP/98xcTE6ODBg/rggw/k8/nMOgBA6OFIE2DRkCFDzJ8dDoeGDRumGTNmaNiwYQE36T2yDgAQOvgnMWDRkCFD9M4770iS6urqdP/99x+3DgAQeghNOOUaGxtVVVVldxsn7PPPP7dct3Pnzk7upmOlpqYqOjra7jYAIKgRmnDKVVVVKScnx+42Os3rr7+u119/3e42TkhRUZEGDBhgdxsAENQITTjlUlNTVVRUZHcbJ6y1tVV5eXmKjY3Vb37zG61YsUK7du1S//79NWvWLC1dulQNDQ16+OGHu9xk8NTUVLtbAICg17X+z46QEB0d3WWPalx77bVavXq17rrrLo0bN067du1Senq67rrrLu3fv19ZWVlKS0uzu00AQCcgNAEn4KabbpIkFRcX6+mnn5YkPf300woLC1NWVpY5DgAIPSw5AJygm266SevWrdO1114r6dDRp3Xr1hGYACDEEZqAdoiMjFRGRoYkKSMjQ5GRkTZ3BADobJyeA4Ag4mz02d0CEFSC6W+C0AQAQcDtdisiMkr6YpPdrQBBJyIySm632+42CE0AEAwSExP19J+eMu9hCHvt2bNH99xzj+644w717dvX7nZOe263W4mJiXa3YW9oWrZsmV544QV9+umniomJ0UUXXaTf/e53Ouecc8wawzC0dOlSPfbYY6qrq9PIkSO1YsUK9e/f36xpbGzUvHnztHr1ajU1NSkzM1OPPPJIwAdcW1ur2bNna+3atXI6nZo8ebIeeughxcXFmTVVVVWaNWuWXn/9dcXFxSk7O1vLli3rcmvuAOiaEhMTg+KLAf9f3759u+wSKeh4tk4E37Rpk3Jzc/X222+rtLRULS0tGjNmjBoaGsya/Px8FRQUqLCwUOXl5YqNjVVmZqYaGxvNmjlz5mjt2rUqLi7Wpk2bVF1drUmTJgXsa+rUqdq2bZtKS0tVUlKizZs3B6xK3dbWpnHjxqm5uVlbtmzRqlWrtHLlSi1ZsqTzPwgAABD8jCCyb98+Q5KxadMmwzAMw+/3G0lJScZ9991n1tTV1RlRUVHGc889Zz6PiIgwiouLzZodO3YYkoyysjLDMAxj+/bthiTj3XffNWvWrVtnOBwO48svvzQMwzBeeeUVw+l0Gl6v16xZsWKF4XK5jKamJkv9+3w+Q5Lh8/na+QmgK6msrDQuueQSo7Ky0u5WAHQw/r5PL1a/v4NqyYHD5/ITEhIkSbt375bX6zUv7ZYOnddMT09XWVmZJKmiokItLS0BNQMHDlRqaqpZU1ZWpvj4eA0fPtysycjIkNPpVHl5uVkzePDggEPjmZmZqq+v17Zt247Zb1NTk+rr6wMeAAAgNAVNaPL7/br11ls1cuRInXvuuZIkr9crSUed409MTDTHvF6vIiMjFR8f/701vXr1ChgPDw9XQkJCQM2x9nNkH/9q2bJlcrvd5iMlJeVE3zYAAOgigiY05ebm6pNPPtHq1avtbsWyhQsXyufzmY+9e/fa3RIAAOgkQRGa8vLyVFJSotdff119+vQxtyclJUmSampqAupramrMsaSkJDU3N6uuru57a/bt2xcw3traqtra2oCaY+3nyD7+VVRUlFwuV8ADAACEJltDk2EYysvL05o1a7Rx40b169cvYLxfv35KSkrShg0bzG319fUqLy+Xx+ORJA0bNkwREREBNZWVlaqqqjJrPB6P6urqVFFRYdZs3LhRfr9f6enpZs3WrVsDwlVpaalcLhd3rQcAAPau05Sbm6tnn31WL730krp3727OHXK73YqJiZHD4dCtt96qu+++W/3791e/fv20ePFiJScna+LEiWbtjBkzNHfuXCUkJMjlcmn27NnyeDwaMWKEJGnQoEEaO3asZs6cqcLCQrW0tCgvL09ZWVlKTk6WJI0ZM0ZpaWmaNm2a8vPz5fV6tWjRIuXm5ioqKsqWzwcAAAQPW0PTihUrJEk//elPA7Y/+eST+uUvfylJWrBggRoaGpSTk6O6ujqNGjVK69evV3R0tFn/wAMPmAtWHrm45ZGeeeYZ5eXlafTo0WZtQUGBOR4WFqaSkhLNmjVLHo9HsbGxys7O1p133tk5bx4AAHQpDsMwDLubCBX19fVyu93y+XzMbzoN7Ny5Uzk5OSoqKmLFYCDE8Pd9erH6/R0UE8EBAACCHaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALAi3uwEAQOhobGxUVVWV3W2ctD179gT8tytLTU1VdHS03W2EBEITAKDDVFVVKScnx+42Osw999xjdwsnraioSAMGDLC7jZBAaAIAdJjU1FQVFRXZ3QaOkJqaancLIYPQBADoMNHR0RzVQMhiIjgAAIAFhCYAAAALCE0AAAAWEJoAAAAsYCJ4F1NTUyOfz2d3G1BoreMSCtxutxITE+1uA0AIcxiGYdjdRKior6+X2+2Wz+eTy+Xq8NevqanRv027Xi3NTR3+2kBXFxEZpaf/9BTBCcAJs/r9zZGmLsTn86mluUkHz75E/mi33e0AQcPZ6JO+2CSfz0doAtBpCE1dkD/aLX/sGXa3AQDAaYWJ4AAAABbYGpo2b96sK6+8UsnJyXI4HHrxxRcDxg3D0JIlS9S7d2/FxMQoIyNDu3btCqhpbGxUbm6uevbsqbi4OE2ePFk1NTUBNbW1tZo6dapcLpfi4+M1Y8YMHThwIKCmqqpK48aNU7du3dSrVy/ddtttam1t7ZT3DQAAuh5bQ1NDQ4OGDh2q5cuXH3M8Pz9fBQUFKiwsVHl5uWJjY5WZmanGxkazZs6cOVq7dq2Ki4u1adMmVVdXa9KkSQGvM3XqVG3btk2lpaUqKSnR5s2bA24o2dbWpnHjxqm5uVlbtmzRqlWrtHLlSi1ZsqRz3jgAAOhygubqOYfDoTVr1mjixImSDh1lSk5O1rx58zR//nxJMid5rly5UllZWfL5fDrzzDP17LPP6pprrpEkffrppxo0aJDKyso0YsQI7dixQ2lpaXr33Xc1fPhwSdL69et1xRVX6B//+IeSk5O1bt06jR8/XtXV1eYk0sLCQt1+++36+uuvFRkZecyem5qa1NT0/69kq6+vV0pKSqddPbdz507l5OSoIe0q5jQBR3A2fKPY7S9zN3cA7WL16rmgndO0e/dueb1eZWRkmNvcbrfS09NVVlYmSaqoqFBLS0tAzcCBA5WammrWlJWVKT4+3gxMkpSRkSGn06ny8nKzZvDgwQFX3WRmZqq+vl7btm07bo/Lli2T2+02HykpKR3z5gEAQNAJ2tDk9Xol6ajLhxMTE80xr9eryMhIxcfHf29Nr169AsbDw8OVkJAQUHOs/RzZx7EsXLhQPp/PfOzdu/cE3yUAAOgqWHLgJERFRSkqKsruNgAAwCkQtEeakpKSJOmoK+FqamrMsaSkJDU3N6uuru57a/bt2xcw3traqtra2oCaY+3nyD4AAMDpLWhDU79+/ZSUlKQNGzaY2+rr61VeXi6PxyNJGjZsmCIiIgJqKisrVVVVZdZ4PB7V1dWpoqLCrNm4caP8fr/S09PNmq1btwaEq9LSUrlcLqWlpXXq+wQAAF2DrafnDhw4oM8++8x8vnv3bn344YdKSEhQamqqbr31Vt19993q37+/+vXrp8WLFys5Odm8ws7tdmvGjBmaO3euEhIS5HK5NHv2bHk8Ho0YMUKSNGjQII0dO1YzZ85UYWGhWlpalJeXp6ysLCUnJ0uSxowZo7S0NE2bNk35+fnyer1atGiRcnNzOf0GAAAk2Rya3nvvPV166aXm87lz50qSsrOztXLlSi1YsEANDQ3KyclRXV2dRo0apfXr1ys6Otr8nQceeEBOp1OTJ09WU1OTMjMz9cgjjwTs55lnnlFeXp5Gjx5t1hYUFJjjYWFhKikp0axZs+TxeBQbG6vs7GzdeeednfwJAACAriJo1mkKBVbXeWgv1mkCjo11mgCcjC6/ThMAAEAwITQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABdx7rgtyHqyzuwUgqPA3AeBUIDR1QTG7N9vdAgAApx1CUxd0sN//kT8m3u42gKDhPFjHPyYAdDpCUxfkj4lnRXAAAE4xJoIDAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAvC7W4AJ87Z6LO7BSCo8DcB4FQgNHUhbrdbEZFR0heb7G4FCDoRkVFyu912twEghBGaupDExEQ9/aen5PPxr+pgsGfPHt1zzz2644471LdvX7vbOe253W4lJiba3QaAEEZo6mISExP5Yggyffv21YABA+xuAwDQyZgIDgAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAF3HsOp1xjY6OqqqrsbuOk7dmzJ+C/XVlqaqqio6PtbgMAghqhCadcVVWVcnJy7G6jw9xzzz12t3DSioqKuOkwAPwvCE045VJTU1VUVGR3GzhCamqq3S0AQNAjNP2L5cuX67777pPX69XQoUP1xz/+URdeeKHdbYWU6OhojmoAALocJoIf4fnnn9fcuXO1dOlSvf/++xo6dKgyMzO1b98+u1sDAAA2IzQd4Q9/+INmzpyp6dOnKy0tTYWFherWrZueeOIJu1sDAAA2IzT9j+bmZlVUVCgjI8Pc5nQ6lZGRobKysmP+TlNTk+rr6wMeAAAgNBGa/sc333yjtrY2JSYmBmxPTEyU1+s95u8sW7ZMbrfbfKSkpJyKVgEAgA0ITSdh4cKF8vl85mPv3r12twQAADoJV8/9jzPOOENhYWGqqakJ2F5TU6OkpKRj/k5UVJSioqJORXsAAMBmHGn6H5GRkRo2bJg2bNhgbvP7/dqwYYM8Ho+NnQEAgGDAkaYjzJ07V9nZ2Ro+fLguvPBCPfjgg2poaND06dPtbg0AANiM0HSE6667Tl9//bWWLFkir9er8847T+vXrz9qcjgAADj9OAzDMOxuIlTU19fL7XbL5/PJ5XLZ3Q4AALDA6vc3c5oAAAAsIDQBAABYQGgCAACwgIngHejw9DBupwIAQNdx+Hv7f5vmTWjqQPv375ckbqcCAEAXtH//frnd7uOOc/VcB/L7/aqurlb37t3lcDjsbgedrL6+XikpKdq7dy9XSwIhhr/v04thGNq/f7+Sk5PldB5/5hJHmjqQ0+lUnz597G4Dp5jL5eJ/qkCI4u/79PF9R5gOYyI4AACABYQmAAAACwhNQDtFRUVp6dKlioqKsrsVAB2Mv28cCxPBAQAALOBIEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAHtsHz5cp111lmKjo5Wenq63nnnHbtbAtABNm/erCuvvFLJyclyOBx68cUX7W4JQYTQBJyg559/XnPnztXSpUv1/vvva+jQocrMzNS+ffvsbg3ASWpoaNDQoUO1fPlyu1tBEGLJAeAEpaen6yc/+YkefvhhSYfuOZiSkqLZs2fr3//9323uDkBHcTgcWrNmjSZOnGh3KwgSHGkCTkBzc7MqKiqUkZFhbnM6ncrIyFBZWZmNnQEAOhuhCTgB33zzjdra2pSYmBiwPTExUV6v16auAACnAqEJAADAAkITcALOOOMMhYWFqaamJmB7TU2NkpKSbOoKAHAqEJqAExAZGalhw4Zpw4YN5ja/368NGzbI4/HY2BkAoLOF290A0NXMnTtX2dnZGj58uC688EI9+OCDamho0PTp0+1uDcBJOnDggD777DPz+e7du/Xhhx8qISFBqampNnaGYMCSA0A7PPzww7rvvvvk9Xp13nnnqaCgQOnp6Xa3BeAkvfHGG7r00kuP2p6dna2VK1ee+oYQVAhNAAAAFjCnCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAToLD4dCLL75odxsATgFCE4CQ8PXXX2vWrFlKTU1VVFSUkpKSlJmZqbfeesvu1gCECG7YCyAkTJ48Wc3NzVq1apXOPvts1dTUaMOGDfr222/tbu2ENDc3KzIy0u42ABwDR5oAdHl1dXX6+9//rt/97ne69NJL1bdvX1144YVauHChrrrqKkmHTqM9+uijGj9+vLp166ZBgwaprKxMn332mX76058qNjZWF110kT7//POA116xYoV++MMfKjIyUuecc47+9Kc/fW8vS5cuVe/evfXxxx9Lkt58801dfPHFiomJUUpKim6++WY1NDSY9WeddZbuuusuXX/99XK5XMrJyengTwdARyE0Aejy4uLiFBcXpxdffFFNTU3HrTscTj788EMNHDhQv/jFL3TjjTdq4cKFeu+992QYhvLy8sz6NWvW6JZbbtG8efP0ySef6MYbb9T06dP1+uuvH/XahmFo9uzZeuqpp/T3v/9dQ4YM0eeff66xY8dq8uTJ+vjjj/X888/rzTffDNiHJP3+97/X0KFD9cEHH2jx4sUd98EA6FAOwzAMu5sAgJP117/+VTNnztTBgwd1wQUX6JJLLlFWVpaGDBki6dCRpkWLFumuu+6SJL399tvyeDx6/PHHdcMNN0iSVq9erenTp+vgwYOSpJEjR+rHP/6xioqKzP1ce+21amho0H/913+Zr1tcXKw1a9bogw8+UGlpqX7wgx9Ikn71q18pLCxMjz76qPn7b775pi655BI1NDQoOjpaZ511ls4//3ytWbOm8z8kACeFI00AQsLkyZNVXV2tl19+WWPHjtUbb7yhCy64QCtXrjRrDgcoSUpMTJQkDR48OGBbY2Oj6uvrJUk7duzQyJEjA/YzcuRI7dixI2DbnDlzVF5ers2bN5uBSZI++ugjrVy50jwSFhcXp8zMTPn9fu3evdusGz58+Ml/AAA6HaEJQMiIjo7WZZddpsWLF2vLli365S9/qaVLl5rjERER5s8Oh+O42/x+/wnt97LLLtOXX36pV199NWD7gQMHdOONN+rDDz80Hx999JF27dqlH/7wh2ZdbGzsCe0PgD24eg5AyEpLSzupNZQGDRqkt956S9nZ2ea2t956S2lpaQF1V111la688kr94he/UFhYmLKysiRJF1xwgbZv364f/ehH7e4BQPAgNAHo8r799ltNmTJFN9xwg4YMGaLu3bvrvffeU35+viZMmNDu173tttt07bXX6vzzz1dGRobWrl2rF154QX/729+Oqr366qv1pz/9SdOmTVN4eLiuueYa3X777RoxYoTy8vL0q1/9SrGxsdq+fbtKS0v18MMPn8xbBmADQhOALi8uLk7p6el64IEH9Pnnn6ulpUUpKSmaOXOm/u///b/tft2JEyfqoYce0u9//3vdcsst6tevn5588kn99Kc/PWb9NddcI7/fr2nTpsnpdGrSpEnatGmT7rjjDl188cUyDEM//OEPdd1117W7JwD24eo5AAAAC5gIDgAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAF/w91/znOeIs2bwAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nsns.boxplot(x=\"smoker\", y=\"charges\", data=df)\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Print the correlation matrix for the dataset.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr(numeric_only=True)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                     Age    Gender       BMI  No_of_Children    Smoker  \\\nAge             1.000000 -0.026046  0.113048        0.037574 -0.023286   \nGender         -0.026046  1.000000  0.042924        0.016020  0.082326   \nBMI             0.113048  0.042924  1.000000       -0.001492  0.011489   \nNo_of_Children  0.037574  0.016020 -0.001492        1.000000  0.006362   \nSmoker         -0.023286  0.082326  0.011489        0.006362  1.000000   \nRegion         -0.007167  0.022213  0.271119       -0.025717  0.054077   \nCharges         0.298624  0.062837  0.199846        0.066442  0.788783   \n\n                  Region   Charges  \nAge            -0.007167  0.298624  \nGender          0.022213  0.062837  \nBMI             0.271119  0.199846  \nNo_of_Children -0.025717  0.066442  \nSmoker          0.054077  0.788783  \nRegion          1.000000  0.054058  \nCharges         0.054058  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>Age</th>\n      <th>Gender</th>\n      <th>BMI</th>\n      <th>No_of_Children</th>\n      <th>Smoker</th>\n      <th>Region</th>\n      <th>Charges</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>1.000000</td>\n      <td>-0.026046</td>\n      <td>0.113048</td>\n      <td>0.037574</td>\n      <td>-0.023286</td>\n      <td>-0.007167</td>\n      <td>0.298624</td>\n    </tr>\n    <tr>\n      <th>Gender</th>\n      <td>-0.026046</td>\n      <td>1.000000</td>\n      <td>0.042924</td>\n      <td>0.016020</td>\n      <td>0.082326</td>\n      <td>0.022213</td>\n      <td>0.062837</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>0.113048</td>\n      <td>0.042924</td>\n      <td>1.000000</td>\n      <td>-0.001492</td>\n      <td>0.011489</td>\n      <td>0.271119</td>\n      <td>0.199846</td>\n    </tr>\n    <tr>\n      <th>No_of_Children</th>\n      <td>0.037574</td>\n      <td>0.016020</td>\n      <td>-0.001492</td>\n      <td>1.000000</td>\n      <td>0.006362</td>\n      <td>-0.025717</td>\n      <td>0.066442</td>\n    </tr>\n    <tr>\n      <th>Smoker</th>\n      <td>-0.023286</td>\n      <td>0.082326</td>\n      <td>0.011489</td>\n      <td>0.006362</td>\n      <td>1.000000</td>\n      <td>0.054077</td>\n      <td>0.788783</td>\n    </tr>\n    <tr>\n      <th>Region</th>\n      <td>-0.007167</td>\n      <td>0.022213</td>\n      <td>0.271119</td>\n      <td>-0.025717</td>\n      <td>0.054077</td>\n      <td>1.000000</td>\n      <td>0.054058</td>\n    </tr>\n    <tr>\n      <th>Charges</th>\n      <td>0.298624</td>\n      <td>0.062837</td>\n      <td>0.199846</td>\n      <td>0.066442</td>\n      <td>0.788783</td>\n      <td>0.054058</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nprint(df.corr())\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Task 4 : Model Development\n\nFit a linear regression model that may be used to predict the `charges` value, just by using the `smoker` attribute of the dataset. Print the $ R^2 $ score of this model.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.linear_model import LinearRegression\nlr=LinearRegression()\nX=df[['Smoker']]\nY=df['Charges']\nlr.fit(X,Y)\nprint(lr.score(X,Y))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "0.6221791733924185\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\nX = df[['smoker']]\nY = df['charges']\nlm = LinearRegression()\nlm.fit(X,Y)\nprint(lm.score(X, Y))\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Fit a linear regression model that may be used to predict the `charges` value, just by using all other attributes of the dataset. Print the $ R^2 $ score of this model. You should see an improvement in the performance.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lr2=LinearRegression()\nx=df[['Age','Gender','BMI','No_of_Children','Region','Smoker']]\nlr2.fit(x,Y)\nprint(lr2.score(x,Y))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "0.7504083820289634\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# definition of Y and lm remain same as used in last cell. \nZ = df[[\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\"]]\nlm.fit(Z,Y)\nprint(lm.score(Z, Y))\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a training pipeline that uses `StandardScaler()`, `PolynomialFeatures()` and `LinearRegression()` to create a model that can predict the `charges` value using all the other attributes of the dataset. There should be even further improvement in the performance.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import PolynomialFeatures\nInput=[('scale',StandardScaler()),('polynomial',PolynomialFeatures(include_bias=False)),('model',LinearRegression())]\npipe=Pipeline(Input)\npipe\nx=x.astype(float)\npipe.fit(x,Y)\nfrom sklearn.metrics import r2_score\nprint(r2_score(Y,pipe.predict(x)))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "0.8441519820122692\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 29
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# Y and Z use the same values as defined in previous cells \nInput=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model', LinearRegression())]\npipe=Pipeline(Input)\nZ = Z.astype(float)\npipe.fit(Z,Y)\nypipe=pipe.predict(Z)\nprint(r2_score(Y,ypipe))\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Task 5 : Model Refinement\n\nSplit the data into training and testing subsets, assuming that 20% of the data will be reserved for testing.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "y_data=df['Charges']\nx_data=df.drop('Charges',axis=1)\nfrom sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test=train_test_split(x_data,y_data,test_size=0.2, random_state=1)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 31
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# Z and Y hold same values as in previous cells\nx_train, x_test, y_train, y_test = train_test_split(Z, Y, test_size=0.2, random_state=1)\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Initialize a Ridge regressor that used hyperparameter $ \\alpha = 0.1 $. Fit the model using training data data subset. Print the $ R^2 $ score for the testing data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.linear_model import Ridge\nR=Ridge(alpha=0.1)\nR.fit(x_train, y_train)\nprint(R.score(x_test,y_test))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "0.6760807731582404\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 35
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# x_train, x_test, y_train, y_test hold same values as in previous cells\nRidgeModel=Ridge(alpha=0.1)\nRidgeModel.fit(x_train, y_train)\nyhat = RidgeModel.predict(x_test)\nprint(r2_score(y_test,yhat))\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Apply polynomial transformation to the training parameters with degree=2. Use this transformed feature set to fit the same regression model, as above, using the training subset. Print the $ R^2 $ score for the testing subset.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pr=PolynomialFeatures(degree=2)\nx_train_pr,x_test_pr=pr.fit_transform(x_train),pr.fit_transform(x_test)\nR.fit(x_train, y_train)\nr2_score(y_test,R.predict(x_test))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6760807731582404"
          },
          "metadata": {}
        }
      ],
      "execution_count": 36
    },
    {
      "cell_type": "markdown",
      "source": "<details>\n    <summary>Click here for Solution</summary>\n\n```python\n# x_train, x_test, y_train, y_test hold same values as in previous cells\npr = PolynomialFeatures(degree=2)\nx_train_pr = pr.fit_transform(x_train)\nx_test_pr = pr.fit_transform(x_test)\nRidgeModel.fit(x_train_pr, y_train)\ny_hat = RidgeModel.predict(x_test_pr)\nprint(r2_score(y_test,y_hat))\n```\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# Congratulations! You have completed this project\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Abhishek Gagneja](https://www.coursera.org/instructor/~129186572)\n\n[Vicky Kuo](https://author.skills.network/instructors/vicky_kuo)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3> Copyright © 2023 IBM Corporation. All rights reserved. </h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<!--## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<!--|Date (YYYY-MM-DD)|Version|Changed By|Change Description|\n|-|-|-|-|\n|2023-09-16|0.1|Abhishek Gagneja|Initial Version Created|\n|2023-09-19|0.2|Vicky Kuo|Reviewed and Revised|\n--!>\n",
      "metadata": {}
    }
  ]
}