Insurance_Cost_Analysis.ipynb

{
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   "metadata": {},
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   "source": [
    "# Importing Required Libraries\n",
    "\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1 : Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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      "text/plain": [
       "    0  1       2  3  4  5            6\n",
       "0  19  1  27.900  0  1  3  16884.92400\n",
       "1  18  2  33.770  1  0  4   1725.55230\n",
       "2  28  2  33.000  3  0  4   4449.46200\n",
       "3  33  2  22.705  0  0  1  21984.47061\n",
       "4  32  2  28.880  0  0  1   3866.85520"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download the dataset to this environment\n",
    "\n",
    "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)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "<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",
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      "text/plain": [
       "  age  gender     bmi  no_of_children smoker  region      charges\n",
       "0  19       1  27.900               0      1       3  16884.92400\n",
       "1  18       2  33.770               1      0       4   1725.55230\n",
       "2  28       2  33.000               3      0       4   4449.46200\n",
       "3  33       2  22.705               0      0       1  21984.47061\n",
       "4  32       2  28.880               0      0       1   3866.85520\n",
       "5  31       1  25.740               0      ?       4   3756.62160\n",
       "6  46       1  33.440               1      0       4   8240.58960\n",
       "7  37       1  27.740               3      0       1   7281.50560\n",
       "8  37       2  29.830               2      0       2   6406.41070\n",
       "9  60       1  25.840               0      0       1  28923.13692"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Add the headers to the dataframe, as mentioned in the project scenario.\n",
    "\n",
    "headers = [\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\", \"charges\"]\n",
    "df.columns = headers\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, replace the '?' entries with 'NaN' values.\n",
    "\n",
    "df.replace('?', np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2 : Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2772 entries, 0 to 2771\n",
      "Data 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\n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 151.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Use dataframe.info() to identify the columns that have some 'Null' (or NaN) information.\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2772 entries, 0 to 2771\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             2772 non-null   int64  \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          2772 non-null   int64  \n",
      " 5   region          2772 non-null   int64  \n",
      " 6   charges         2772 non-null   float64\n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 151.7 KB\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_2616\\863333469.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The 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",
      "\n",
      "For 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, is_smoker, inplace=True)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_2616\\863333469.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The 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",
      "\n",
      "For 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, mean_age, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# smoker is a categorical attribute, replace with most frequent entry\n",
    "is_smoker = df['smoker'].value_counts().idxmax()\n",
    "df[\"smoker\"].replace(np.nan, is_smoker, inplace=True)\n",
    "\n",
    "# age is a continuous variable, replace with mean age\n",
    "mean_age = df['age'].astype('float').mean(axis=0)\n",
    "df[\"age\"].replace(np.nan, mean_age, inplace=True)\n",
    "\n",
    "# Update data types\n",
    "df[[\"age\",\"smoker\"]] = df[[\"age\",\"smoker\"]].astype(\"int\")\n",
    "\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<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>16884.92</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>1725.55</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>4449.46</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>21984.47</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>3866.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender     bmi  no_of_children  smoker  region   charges\n",
       "0   19       1  27.900               0       1       3  16884.92\n",
       "1   18       2  33.770               1       0       4   1725.55\n",
       "2   28       2  33.000               3       0       4   4449.46\n",
       "3   33       2  22.705               0       0       1  21984.47\n",
       "4   32       2  28.880               0       0       1   3866.86"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update the charges column such that all values are rounded to nearest 2 decimal places.\n",
    "\n",
    "df[[\"charges\"]] = np.round(df[[\"charges\"]],2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3 : Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 66902.85800000001)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Implement the regression plot for charges with respect to bmi\n",
    "\n",
    "sns.regplot(x=\"bmi\", y=\"charges\", data=df, line_kws={\"color\": \"red\"})\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='smoker', ylabel='charges'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Implement the box plot for charges with respect to smoker.\n",
    "\n",
    "sns.boxplot(x=\"smoker\", y=\"charges\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "                     age    gender       bmi  no_of_children    smoker  \\\n",
       "age             1.000000 -0.026046  0.113048        0.037574 -0.023286   \n",
       "gender         -0.026046  1.000000  0.042924        0.016020  0.082326   \n",
       "bmi             0.113048  0.042924  1.000000       -0.001492  0.011489   \n",
       "no_of_children  0.037574  0.016020 -0.001492        1.000000  0.006362   \n",
       "smoker         -0.023286  0.082326  0.011489        0.006362  1.000000   \n",
       "region         -0.007167  0.022213  0.271119       -0.025717  0.054077   \n",
       "charges         0.298624  0.062837  0.199846        0.066442  0.788783   \n",
       "\n",
       "                  region   charges  \n",
       "age            -0.007167  0.298624  \n",
       "gender          0.022213  0.062837  \n",
       "bmi             0.271119  0.199846  \n",
       "no_of_children -0.025717  0.066442  \n",
       "smoker          0.054077  0.788783  \n",
       "region          1.000000  0.054058  \n",
       "charges         0.054058  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the correlation matrix for the dataset.\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4 : Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6221791733924185\n"
     ]
    }
   ],
   "source": [
    "# Fit 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",
    "\n",
    "X = df[['smoker']]\n",
    "Y = df['charges']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "print(lm.score(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7504083820289634\n"
     ]
    }
   ],
   "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",
    "\n",
    "# definition of Y and lm remain same as used in last cell. \n",
    "Z = df[[\"age\", \"gender\", \"bmi\", \"no_of_children\", \"smoker\", \"region\"]]\n",
    "lm.fit(Z,Y)\n",
    "print(lm.score(Z, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8452515297015821\n"
     ]
    }
   ],
   "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",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "# Y and Z use the same values as defined in previous cells \n",
    "Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model', LinearRegression())]\n",
    "pipe=Pipeline(Input)\n",
    "Z = Z.astype(float)\n",
    "pipe.fit(Z,Y)\n",
    "ypipe=pipe.predict(Z)\n",
    "print(r2_score(Y,ypipe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5 : Model Refinement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing subsets, assuming that 20% of the data will be reserved for testing\n",
    "\n",
    "# Z and Y hold same values as in previous cells\n",
    "x_train, x_test, y_train, y_test = train_test_split(Z, Y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6760807731582404\n"
     ]
    }
   ],
   "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",
    "\n",
    "# x_train, x_test, y_train, y_test hold same values as in previous cells\n",
    "RidgeModel=Ridge(alpha=0.1)\n",
    "RidgeModel.fit(x_train, y_train)\n",
    "yhat = RidgeModel.predict(x_test)\n",
    "print(r2_score(y_test,yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7835631107608146\n"
     ]
    }
   ],
   "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",
    "\n",
    "# x_train, x_test, y_train, y_test hold same values as in previous cells\n",
    "pr = PolynomialFeatures(degree=2)\n",
    "x_train_pr = pr.fit_transform(x_train)\n",
    "x_test_pr = pr.fit_transform(x_test)\n",
    "RidgeModel.fit(x_train_pr, y_train)\n",
    "y_hat = RidgeModel.predict(x_test_pr)\n",
    "print(r2_score(y_test,y_hat))"
   ]
  }
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