Medical Cost Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "Today we will explore a data set dedicated to the cost of treatment of different patients. The cost of treatment depends on many factors: diagnosis, type of clinic, city of residence, age and so on. We have no data on the diagnosis of patients. But we have other information that can help us to make a conclusion about the health of patients and practice regression analysis\n",
    "\n",
    "## Content\n",
    "### Columns\n",
    "\n",
    "1. age: age of primary beneficiary\n",
    "\n",
    "2. sex: insurance contractor gender, female, male\n",
    "\n",
    "3. bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,\n",
    "   objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9\n",
    "\n",
    "4. children: Number of children covered by health insurance / Number of dependents\n",
    "\n",
    "5. smoker: Smoking\n",
    "\n",
    "6. region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.\n",
    "\n",
    "7. charges: Individual medical costs billed by health insurance\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, Import the data stored in SQL Database.\n",
    "- make a odbc connection\n",
    "- read as a pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyodbc \n",
    "conn = pyodbc.connect('Driver={SQL Server};'\n",
    "                      'Server=DESKTOP-FV1I7CQ;'\n",
    "                      \"uid=SanketDatabase;pwd=patil\")\n",
    "\n",
    "cursor = conn.cursor()\n",
    "\n",
    "df = pd.read_sql_query('Select * from [Medical Cost Regression].dbo.[insurance]',conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's Look at the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>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>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi children smoker     region      charges\n",
       "0   19  female  27.900        0    yes  southwest  16884.92400\n",
       "1   18    male  33.770        1     no  southeast   1725.55230\n",
       "2   28    male  33.000        3     no  southeast   4449.46200\n",
       "3   33    male  22.705        0     no  northwest  21984.47061\n",
       "4   32    male  28.880        0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial plan for data exploration\n",
    "- Overview the data\n",
    "- check datatypes of the features\n",
    "- check null values and take action\n",
    "- Feature Engineering (One-hot-encoding, skewness check)\n",
    "- Data Exploration (Visual Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1338, 7)\n",
      "['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges']\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have 1338 customer data and 7 columns including target variable charges\n",
    "- Now we should look at datatype for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age           int64\n",
      "sex          object\n",
      "bmi         float64\n",
      "children     object\n",
      "smoker       object\n",
      "region       object\n",
      "charges     float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "bmi         0\n",
       "children    0\n",
       "smoker      0\n",
       "region      0\n",
       "charges     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good day is when there is no \"NaN\"in the data :D Let's look at our data to understand something about it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>1338</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>1338</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <td>1338</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>1338</td>\n",
       "      <td>4</td>\n",
       "      <td>southeast</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         count unique        top  freq\n",
       "sex       1338      2       male   676\n",
       "children  1338      6          0   574\n",
       "smoker    1338      2         no  1064\n",
       "region    1338      4  southeast   364"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charges</th>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unique Values\n",
       "Variable               \n",
       "age                  47\n",
       "sex                   2\n",
       "bmi                 548\n",
       "children              6\n",
       "smoker                2\n",
       "region                4\n",
       "charges            1337"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uniques = pd.DataFrame([[i, len(df[i].unique())] for i in df.columns], columns=['Variable', 'Unique Values']).set_index('Variable')\n",
    "df_uniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of binary freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sex', 'smoker']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_variables = list(df_uniques[df_uniques['Unique Values'] == 2].index)\n",
    "binary_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of categorical freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['children', 'region']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_variables = list(df_uniques[(6 >= df_uniques['Unique Values']) & (df_uniques['Unique Values'] > 2)].index)\n",
    "categorical_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- check the unique levels in categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['children', ['0', '1', '3', '2', '5', '4']],\n",
       " ['region', ['southwest', 'southeast', 'northwest', 'northeast']]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[i, list(df[i].unique())] for i in categorical_variables]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Discrete numbers , aka integers, can be used to represent both discrete numeric variables and ordinal variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_variables = ['children']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- convert catogorical features in numeric levels respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer, LabelEncoder, OrdinalEncoder\n",
    "lb, le = LabelBinarizer(), LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        children\n",
       "count       1338\n",
       "unique         6\n",
       "top            0\n",
       "freq         574"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in ordinal_variables:\n",
    "    df[column] = le.fit_transform(df[column])\n",
    "\n",
    "df[ordinal_variables].astype('category').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1338.000000</td>\n",
       "      <td>1338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.505232</td>\n",
       "      <td>0.204783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500160</td>\n",
       "      <td>0.403694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sex       smoker\n",
       "count  1338.000000  1338.000000\n",
       "mean      0.505232     0.204783\n",
       "std       0.500160     0.403694\n",
       "min       0.000000     0.000000\n",
       "25%       0.000000     0.000000\n",
       "50%       1.000000     0.000000\n",
       "75%       1.000000     0.000000\n",
       "max       1.000000     1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for column in binary_variables:\n",
    "    df[column] = lb.fit_transform(df[column])\n",
    "\n",
    "df[binary_variables].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_variables = list(set(categorical_variables) - set(ordinal_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex     bmi  children  smoker      charges  region_northwest  \\\n",
       "0   19    0  27.900         0       1  16884.92400                 0   \n",
       "1   18    1  33.770         1       0   1725.55230                 0   \n",
       "2   28    1  33.000         3       0   4449.46200                 0   \n",
       "3   33    1  22.705         0       0  21984.47061                 1   \n",
       "4   32    1  28.880         0       0   3866.85520                 1   \n",
       "\n",
       "   region_southeast  region_southwest  \n",
       "0                 0                 1  \n",
       "1                 1                 0  \n",
       "2                 1                 0  \n",
       "3                 0                 0  \n",
       "4                 0                 0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.get_dummies(df, columns = categorical_variables, drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a list of Numeric freatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['region_southeast', 'age', 'bmi', 'charges', 'region_northwest', 'region_southwest']\n"
     ]
    }
   ],
   "source": [
    "numeric_variables = list(set(df.columns) - set(ordinal_variables) - set(categorical_variables) - set(binary_variables))\n",
    "print(numeric_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- check the skewness of the Numerical features and target; charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'region_southeast'}>,\n",
       "        <AxesSubplot:title={'center':'age'}>],\n",
       "       [<AxesSubplot:title={'center':'bmi'}>,\n",
       "        <AxesSubplot:title={'center':'charges'}>],\n",
       "       [<AxesSubplot:title={'center':'region_northwest'}>,\n",
       "        <AxesSubplot:title={'center':'region_southwest'}>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[numeric_variables].hist(figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region_southeast = 1.0244709894710085\n",
      "age = 0.055610083072599126\n",
      "bmi = 0.28372857291709386\n",
      "charges = 1.5141797118745743\n",
      "region_northwest = 1.1990630913597837\n",
      "region_southwest = 1.1990630913597833\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew \n",
    "for i in numeric_variables:\n",
    "    print(i + \" =\", skew(df[i])) # log1p,sqrt,cbrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- age is near to the normal distribution\n",
    "- bmi is skewed right skewed right, meaning that the right tail of the distribution is longer than the left\n",
    "- Charges is positively skewed  \n",
    "\n",
    "### Action plan\n",
    "- for bmi perform square root transformation\n",
    "- for charges perform Box-Cox target transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness = 0.014147211230111848\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAASnElEQVR4nO3df6zddX3H8edbYAy5CiW4u65tdlnWmQGNKDdVQ2LulU1QjMVElhLmirLULbho1mQr/jFdTBP+GP7YELdq1S6odx3KaPjhxjpvmIkMKUMvpRI7uWJb106F4mUEc/G9P+4Xdrg9t+fc8/t8+nwkN+d7Pt8f53U/vffVc7/3nO+NzESSVJaX9TuAJKnzLHdJKpDlLkkFstwlqUCWuyQV6NR+BwA499xzc2xsrOX9n3nmGc4888zOBeoRc/fesGY3d28NS+69e/f+ODNfVW/dQJT72NgYDz74YMv7T09PMzEx0blAPWLu3hvW7OburWHJHRE/WGqdp2UkqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgRqWe0T8ckQ8EBHfjoh9EfGX1fg5EXFvRHyvul1Rs88NEXEgIh6LiMu6+QlIko7XzDP354A3Z+ZrgIuAyyPiDcBWYE9mrgX2VPeJiPOBjcAFwOXALRFxSheyS5KW0LDcc8Fcdfe06iOBDcDOanwncGW1vAGYysznMvNx4ACwvpOhJUknFs38sY7qmfde4DeBT2Xmn0fEU5l5ds02T2bmioi4Gbg/M2+txncA92TmbYuOuRnYDDA6Onrx1NRUy5/E3NwcIyMjLe/fL+Zu3syhYx05zugZcOTZ5e2zbtVZHXnsdvi10lvDkntycnJvZo7XW9fU5Qcy83ngoog4G7g9Ii48weZR7xB1jrkd2A4wPj6e7bzVd1jeKryYuZt37da7OnKcLevmuWlmeVfdmL1moiOP3Q6/VnprWHPXWtarZTLzKWCahXPpRyJiJUB1e7Ta7CCwpma31cDhdoNKkprXzKtlXlU9YycizgB+B/gusBvYVG22CbijWt4NbIyI0yPiPGAt8ECHc0uSTqCZn09XAjur8+4vA3Zl5p0R8U1gV0RcBzwBXAWQmfsiYhfwKDAPXF+d1pEk9UjDcs/M7wCvrTP+E+DSJfbZBmxrO50kqSW+Q1WSCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoGa+Ruq0kltbOtdfXnc2Ruv6Mvjqgw+c5ekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUANyz0i1kTE1yNif0Tsi4gPVOMfiYhDEfFw9fG2mn1uiIgDEfFYRFzWzU9AknS8Zt7ENA9sycyHIuIVwN6IuLda9/HM/KvajSPifGAjcAHwa8C/RsRvZebznQwuSVpaw2fumfmjzHyoWv4ZsB9YdYJdNgBTmflcZj4OHADWdyKsJKk5kZnNbxwxBtwHXAj8KXAt8DTwIAvP7p+MiJuB+zPz1mqfHcA9mXnbomNtBjYDjI6OXjw1NdXyJzE3N8fIyEjL+/fLMOaeOXSM0TPgyLP9TtKaYcq+btVZLy4P49cKmLvbJicn92bmeL11TV9bJiJGgK8AH8zMpyPi08BHgaxubwLeC0Sd3Y/7HyQztwPbAcbHx3NiYqLZKMeZnp6mnf37ZRhzX7v1Lrasm+emmeG8LNEwZZ+9ZuLF5WH8WgFz91NTr5aJiNNYKPYvZuZXATLzSGY+n5m/AD7D/596OQisqdl9NXC4c5ElSY0082qZAHYA+zPzYzXjK2s2eyfwSLW8G9gYEadHxHnAWuCBzkWWJDXSzM+nlwDvBmYi4uFq7EPA1RFxEQunXGaB9wFk5r6I2AU8ysIrba73lTKS1FsNyz0zv0H98+h3n2CfbcC2NnJJktrgO1QlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUANyz0i1kTE1yNif0Tsi4gPVOPnRMS9EfG96nZFzT43RMSBiHgsIi7r5icgSTpeM8/c54EtmfnbwBuA6yPifGArsCcz1wJ7qvtU6zYCFwCXA7dExCndCC9Jqq9huWfmjzLzoWr5Z8B+YBWwAdhZbbYTuLJa3gBMZeZzmfk4cABY3+HckqQTiMxsfuOIMeA+4ELgicw8u2bdk5m5IiJuBu7PzFur8R3APZl526JjbQY2A4yOjl48NTXV8icxNzfHyMhIy/v3yzDmnjl0jNEz4Miz/U7SmmHKvm7VWS8uD+PXCpi72yYnJ/dm5ni9dac2e5CIGAG+AnwwM5+OiCU3rTN23P8gmbkd2A4wPj6eExMTzUY5zvT0NO3s3y/DmPvarXexZd08N800/aUzUIYp++w1Ey8uD+PXCpi7n5p6tUxEnMZCsX8xM79aDR+JiJXV+pXA0Wr8ILCmZvfVwOHOxJUkNaOZV8sEsAPYn5kfq1m1G9hULW8C7qgZ3xgRp0fEecBa4IHORZYkNdLMz6eXAO8GZiLi4WrsQ8CNwK6IuA54ArgKIDP3RcQu4FEWXmlzfWY+3+ngkqSlNSz3zPwG9c+jA1y6xD7bgG1t5JIktcF3qEpSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVKDhuMiGdBIa23rXi8tb1s1zbc39bpu98YqePZa6w2fuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAXvJ3CI318NKvkoaTz9wlqUCWuyQVqGG5R8TnIuJoRDxSM/aRiDgUEQ9XH2+rWXdDRByIiMci4rJuBZckLa2ZZ+5fAC6vM/7xzLyo+rgbICLOBzYCF1T73BIRp3QqrCSpOQ3LPTPvA37a5PE2AFOZ+VxmPg4cANa3kU+S1IJ2zrm/PyK+U522WVGNrQJ+WLPNwWpMktRDkZmNN4oYA+7MzAur+6PAj4EEPgqszMz3RsSngG9m5q3VdjuAuzPzK3WOuRnYDDA6Onrx1NRUy5/E3NwcIyMjLe/fL63mnjl0rAtpmjd6Bhx5tq8RWjas2Xude92qszpynJPte7PXJicn92bmeL11Lb3OPTOPvLAcEZ8B7qzuHgTW1Gy6Gji8xDG2A9sBxsfHc2JiopUoAExPT9PO/v3Sau5r+/w69y3r5rlpZjjfIjGs2Xude/aaiY4c52T73hwkLZ2WiYiVNXffCbzwSprdwMaIOD0izgPWAg+0F1GStFwNnwpExJeBCeDciDgIfBiYiIiLWDgtMwu8DyAz90XELuBRYB64PjOf70pySdKSGpZ7Zl5dZ3jHCbbfBmxrJ5QkqT2+Q1WSCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBGpZ7RHwuIo5GxCM1Y+dExL0R8b3qdkXNuhsi4kBEPBYRl3UruCRpac08c/8CcPmisa3AnsxcC+yp7hMR5wMbgQuqfW6JiFM6llaS1JSG5Z6Z9wE/XTS8AdhZLe8ErqwZn8rM5zLzceAAsL4zUSVJzYrMbLxRxBhwZ2ZeWN1/KjPPrln/ZGauiIibgfsz89ZqfAdwT2beVueYm4HNAKOjoxdPTU21/EnMzc0xMjLS8v790mrumUPHupCmeaNnwJFn+xqhZcOavde51606qyPHOdm+N3ttcnJyb2aO11t3aocfK+qM1f3fIzO3A9sBxsfHc2JiouUHnZ6epp39+6XV3NduvavzYZZhy7p5bprp9JdObwxr9l7nnr1moiPHOdm+NwdJq6+WORIRKwGq26PV+EFgTc12q4HDrceTJLWi1XLfDWyqljcBd9SMb4yI0yPiPGAt8EB7ESVJy9Xw57yI+DIwAZwbEQeBDwM3Arsi4jrgCeAqgMzcFxG7gEeBeeD6zHy+S9klSUtoWO6ZefUSqy5dYvttwLZ2QkmS2uM7VCWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUoOH7qwWSum6sQ38QZsu6+WX9cZnZG6/oyOPKZ+6SVCTLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBvLZMG9q9/sZyr7shSc1qq9wjYhb4GfA8MJ+Z4xFxDvAPwBgwC/xeZj7ZXkxJ0nJ04rTMZGZelJnj1f2twJ7MXAvsqe5LknqoG+fcNwA7q+WdwJVdeAxJ0glEZra+c8TjwJNAAn+Xmdsj4qnMPLtmmyczc0WdfTcDmwFGR0cvnpqaajnH3NwcIyMjLe/fqplDx9raf/QMOPJsh8L00LDmhuHNfrLkXrfqrO6FWYZ+dcpyTU5O7q05a/IS7f5C9ZLMPBwRvwLcGxHfbXbHzNwObAcYHx/PiYmJlkNMT0/Tzv6taveXoVvWzXPTzPD9TntYc8PwZj9Zcs9eM9G9MMvQr07ppLZOy2Tm4er2KHA7sB44EhErAarbo+2GlCQtT8vlHhFnRsQrXlgG3gI8AuwGNlWbbQLuaDekJGl52vk5bxS4PSJeOM6XMvNrEfEtYFdEXAc8AVzVfkxJ0nK0XO6Z+X3gNXXGfwJc2k4oSVJ7vPyAJBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgYbvT7tIKtZYm3/drFWzN17Rl8ftpiLKfebQsbb/5J0klcTTMpJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoG6dm2ZiLgc+CRwCvDZzLyxW48lSe1YfMGyLevme3a9qm5dtKwrz9wj4hTgU8BbgfOBqyPi/G48liTpeN06LbMeOJCZ38/MnwNTwIYuPZYkaZHIzM4fNOJdwOWZ+YfV/XcDr8/M99dssxnYXN19NfBYGw95LvDjNvbvF3P33rBmN3dvDUvuX8/MV9Vb0a1z7lFn7CX/i2TmdmB7Rx4s4sHMHO/EsXrJ3L03rNnN3VvDmrtWt07LHATW1NxfDRzu0mNJkhbpVrl/C1gbEedFxC8BG4HdXXosSdIiXTktk5nzEfF+4J9ZeCnk5zJzXzceq9KR0zt9YO7eG9bs5u6tYc39oq78QlWS1F++Q1WSCmS5S1KBhqrcI+KUiPjPiLizzrqIiL+OiAMR8Z2IeF0/Mi6lQfaJiDgWEQ9XH3/Rj4yLRcRsRMxUmR6ss34g57yJ3AM53wARcXZE3BYR342I/RHxxkXrB3XOG+UeuDmPiFfX5Hk4Ip6OiA8u2mYg57sZXbu2TJd8ANgPvLLOurcCa6uP1wOfrm4HxYmyA/x7Zr69h3maNZmZS72ZY5Dn/ES5YXDn+5PA1zLzXdUrzV6+aP2gznmj3DBgc56ZjwEXwYuXTDkE3L5os0Gd74aG5pl7RKwGrgA+u8QmG4C/zwX3A2dHxMqeBTyBJrIPq4Gd82EUEa8E3gTsAMjMn2fmU4s2G7g5bzL3oLsU+K/M/MGi8YGb72YNTbkDnwD+DPjFEutXAT+suX+wGhsEn+DE2QHeGBHfjoh7IuKC3sRqKIF/iYi91eUiFhvUOW+UGwZzvn8D+B/g89UpvM9GxJmLthnEOW8mNwzmnL9gI/DlOuODON9NGYpyj4i3A0czc++JNqsz1vfXeTaZ/SEWrhHxGuBvgH/qRbYmXJKZr2PhR9PrI+JNi9YP5JzTOPegzvepwOuAT2fma4FngK2LthnEOW8m96DOOdVppHcA/1hvdZ2xfs93U4ai3IFLgHdExCwLV5h8c0TcumibQb3kQcPsmfl0Zs5Vy3cDp0XEuT1PukhmHq5uj7JwLnL9ok0Gcs4b5R7U+WZhPg9m5n9U929joTQXbzNoc94w9wDPOSw8CXgoM4/UWTeI892UoSj3zLwhM1dn5hgLPz79W2b+/qLNdgN/UP12+w3Ascz8Ua+zLtZM9oj41YiIank9C/8uP+l52JdmOjMiXvHCMvAW4JFFmw3cnDeTexDnGyAz/xv4YUS8uhq6FHh00WYDN+fN5B7UOa9cTf1TMjCA892sYXu1zEtExB8BZObfAncDbwMOAP8LvKeP0RpalP1dwB9HxDzwLLAx+//W4VHg9ur78VTgS5n5tSGY82ZyD+J8v+BPgC9Wpwq+D7xnCOYcGuceyDmPiJcDvwu8r2ZsGOa7IS8/IEkFGorTMpKk5bHcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoH+D5Wfa7d7597wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.sqrt(df['bmi']).hist()\n",
    "print(\"Skewness =\",np.sqrt(df['bmi']).skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bmi']= np.sqrt(df['bmi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target transformation\n",
    "- The box cox transformation is a parametrized transformation that tries to get distributions \"as close to a normal distribution as possible\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.043649053770664956"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import boxcox\n",
    "bc_result = boxcox(df.charges)\n",
    "boxcox_charges = bc_result[0]\n",
    "lam = bc_result[1]\n",
    "lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 64.,  97., 113., 164., 221., 278., 135., 104., 115.,  47.]),\n",
       " array([ 8.21807622,  8.81841614,  9.41875606, 10.01909598, 10.6194359 ,\n",
       "        11.21977582, 11.82011575, 12.42045567, 13.02079559, 13.62113551,\n",
       "        14.22147543]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+CdgDXMLcG/GNVbXQH5MmxiLzeAb4B2AK+G/gkaq6ZkwR+7bIXG4DzgN+0K321ap6x1gCLsMic7kO+A3geeB7wDu6v8NNrIXmUVV39Cw/zNzZf2f9o4wnrtwlScObtMMykqQVYLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBv0/GOy6c9lP60UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(boxcox_charges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- this target transformation method we will use in later stage of this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Analytics\n",
    "- plot corr heatmap \n",
    "- see age distribution in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(240,10,as_cmap=True),\n",
    "            square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Age Distribution'}, xlabel='age'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g5CRfSfJ14KylH7I0P78Jp4mV5NCq+uF4B3wB8I6qumC+t5OWC++ANcleOf7tvMsY/mDjh1pHIy2Qd8CS1MQ7YElqYoAlqYkBlqQmBliSmhhgSWryv9+OWUeXqvMDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Age Distribution')\n",
    "sns.boxplot(df.age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "50% of people from this dataset have ages between 28-53\n",
    "- A strong correlation is observed only with the fact of smoking the patient. Although, I expected a higher correlation with bmi; We investigate smoking in more detai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male= 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"smoker\", kind=\"count\",hue = 'sex', palette=\"pink\", data=df)\n",
    "print(\"Male= 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we can notice that more male smokers than women smokers\n",
    "- lets see genderwise charges for smokers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Box plot for charges of men'}, xlabel='charges', ylabel='smoker'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.title(\"Box plot for charges of men\")\n",
    "sns.boxplot(y=\"smoker\", x=\"charges\", data =  df[(df.sex == 0)] , orient=\"h\", palette = 'magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Box plot for charges of women'}, xlabel='charges', ylabel='smoker'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.title(\"Box plot for charges of women\")\n",
    "sns.boxplot(y=\"smoker\", x=\"charges\", data =  df[(df.sex == 1)] , orient=\"h\", palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- nonsmokers get less charges but smokers get high charges; female smokers charged more \n",
    "- Now see BMI, which I think would be important in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yLCKHbbRxlLbr2siozaDyg5WYT8X5Ob6Aj7U/XYsLOXb90y58aT6qL9Fc0CIiiUrlWUQOy0THBK1XtxIoCFB1SRW+oC6lmGlWkcLXFTLaMMqzlz7LwN8GyD85P+79VW2umst4IiJyGPRbTkQOWWgwxL7v7AOg+qPVBHL19/iBWMCo/FAlWeuyaL+xneFtw15HEhGRQ6DyLCKHxIUcrde0EuoLUXVJFcHyoNeRFj1fmo+qD1eRXpVO2w/aGG8Z9zqSiIjMksqziBySjls6GNs1RsVFFWSuyPQ6TsLwZfiourQKX4aPfd/bx2TvpNeRRERkFlSeRWTW+v7QR/+f+ik6o4jcDblex0k4aYVpVF9aTXgkTOvVrUTGIl5HEhGROKk8i8isjDw7QsctHWQfmU3xxmKv4ySs9Jp0qjZXMb5vnNYftuLCzutIIiISB5VnEYnbZM8kbT9oI600jYoPVmhKusOUfWR0TuyRp0fovL3T6zgiIhIHXRovInGJTERovaYVN+mo/kg1/ky/15GSQsGrC5honaDvN31kLM8g7/g8ryOJiMhL0JlnEYlLx80djDePU/GBCoIVmlljLpW+s5TMVZm039DO+D7NwCEispipPIvIQfX/pZ+BBwcoelMROUfneB0n6ZjfqNxciS/TR+u1rYRHwl5HEhGRA1B5FpGXNN48TsfPOsham0Xx2bpAcL4E8gNUfbiKya5J9v9oPy6iCwhFRBYjlWcROaDwaJjWLa34sn1UvF8XCM63zJWZlJ1TxvBTw/T8X4/XcUREZAYqzyIyI+cc7T9pZ7JrkqoPVRHI0/XFCyH/1HzyTsqj+1fduoW3iMgipPIsIjNquaqFob8PUfL2EjJX6Q6CC8XMKHtvGcGKIPt/vJ/QQMjrSCIiMoXKs4i8yMDDAzR+upHsY7MpfH2h13FSji/oo/JDlURGI+z/scY/i4gsJirPIvICk32TbNu0jfQl6VRcWIGZxjl7Ib06ndJzShnZNkLzfzZ7HUdERGJUnkXkec45dm7eyVjzGGt/thZ/lm6E4qX8V+eT8/Icdn9uN/0P9XsdR0REUHkWkSnarmuj8+edrLhyBfkn5XsdJ+WZGeXnlZO+JJ1tm7Yx2TfpdSQRkZSn8iwiAAw/M0zDxxoofEMhNZ+u8TqOxPiz/Ky7ZR0T+ybY+aGdOKfxzyIiXlJ5FhHCo2Geefcz+PP8HHHDEZrPeZHJOzGP5Vcup/O2TvZfv9/rOCIiKU3lWURo+KcGRp4ZYe1P15Jeke51HJlBzSdrKDi1gIaPNTC6a9TrOCIiKUvlWSTFdf6ik7YftFHzmRqKTi/yOo4cgPkt+qlAwKg/r55IKOJ1JBGRlBRXeTazM8xsh5k1mNllM6w3M/tObP2TZrZ+yro9ZvaUmT1uZlvnMryIHJ7x1nF2fGgHOetzWP7l5V7HkYPIqMlg9bWrGXhogKYrm7yOIyKSkg5ans3MD1wNnAmsA841s3XTNjsTqIt9bQaumbb+tc65Y51zGw4/sojMBRdxbL9oO5GRCOtuXocvqA+iEkHZu8soP7+cPV/eo+nrREQ8EM9vyxOABudco3NuArgF2Dhtm43ADS7qIaDAzCrnOKuIzKGWq1rofaCXVf+1iqw1WV7HkVmo+24d6UvSqX9vPaFB3b5bRGQhxVOeq4Gpt7dqiS2LdxsH3G9mj5rZ5gO9iZltNrOtZra1s7MzjlgicqiGnhyi8bJGijcWU/kh/Z2baAL5AdbeuJaxPWM0fLzB6zgiIiklEMc2M81ZNX2i0Zfa5mTnXKuZlQEPmNl259wfX7Sxc1uALQAbNmzQRKYih6B1S+tBt4lMRGj6WhO+DB/5r86n7YdtC5BM5lrBqwpYevlSmq5sovisYkrfUep1JBGRlBDPmecWYOodE5YA039DH3Ab59xz3zuAXxAdBiIiHun6RRcTrRNUXFRBIDeev59lsar9Yi25G3LZsXkH4/vGvY4jIpIS4inPjwB1ZrbczILAJuDOadvcCVwQm3XjJKDfOddmZtlmlgtgZtnA6cDTc5hfRGZhuH6Yvt/2UfC6ArJflu11HDlMvjQfa29aS2QswvaLtuMi+tBORGS+HbQ8O+dCwKXAfUA9cKtz7hkzu9jMLo5tdg/QCDQAPwQ+ElteDvzZzJ4A/gbc7Zy7d45/BhGJQ3gkTPtP2glWBCl5W4nXcWSOZK3OYtW3V9H7615avtPidRwRkaQX12e2zrl7iBbkqcuunfLYAZfM8LpG4JjDzCgic6DjfzoI9YdY+i9LNS1dkqn8UCXdd3fTeFkjhacVknNUjteRRESSln6DiqSAwccGGXxokKIzi8iozfA6jswxM2PNdWsIFASof0894bGw15FERJKWyrNIkgsNhOi4qYP0pekUn1XsdRyZJ8HSIEf8+AiGnx5m9+W7vY4jIpK0VJ5FkphzjvafthMZi1DxvgrMP9OskpIsis8spvrSalr+q4WeB3q8jiMikpRUnkWS2MBfBxh+cpiSt5WQXpXudRxZACv+fQVZa7PYftF2JrsnvY4jIpJ0VJ5FktRk7ySdP+8ksy6TgtcVeB1HFog/08/am9cy2TnJjs07iF7PLSIic0XlWSQJOefouLEDF3KUX1iO+TRcI5XkHpvL8iuX03VHF/uv3+91HBGRpKLyLJKEBh4aYPjp6HCNYGnQ6zjigZpP1lDw2gIaPtbA6K5Rr+OIiCQNlWeRJBPqC9F5ayeZqzIpeG2B13HEI+YzjvjJEVjAqD+vnkgo4nUkEZGkoPIskkScc7Tf1I6bdJRfoOEaqS6jJoPV165m4KEB9n5lr9dxRESSgsqzSBIZ/NtgdHaNjSUEyzVcQ6Ds3WWUn1/O3q/spf/Bfq/jiIgkPJVnkSQxvn+cjls6yFiRQcFpBV7HkUWk7rt1pC9Jp/68ekKDIa/jiIgkNJVnkSTR8NEG3ISj4sIKDdeQFwjkB1h741rG9ozR8PEGr+OIiCQ0lWeRJNB1Zxedt3VSdFYRwQoN15AXK3hVAUsvX8r+H++n8/ZOr+OIiCQslWeRBBcaCPHsJc+SfWQ2RacXeR1HFrHaL9aSuyGXHZt3ML5v3Os4IiIJKeB1ABE5PLs/v5vxfeOs+/k6hp8c9jqOzIPWLa1ztq+1N61l63Fb2X7Rdo6+72gN8RERmSWdeRZJYP0P9bPve/uovqSa/JPyvY4jCSBrdRarvr2K3l/30nJVi9dxREQSjsqzSIKKTEbY+aGdpFens/yry72OIwmk8kOVFL+lmMbLGhl6asjrOCIiCUXlWSRBNf9HM8NPD1P3/ToCuRqBJfEzM9Zct4ZAYYD699QTHgt7HUlEJGHoN65IAhppGGHPFXsofVcpJW8u8TqOJJCp46fLzilj33f38dTZT1F2Ttkh7a9qc9VcRRMRSQg68yySYJxzPHvps/iCPlZdtcrrOJLAso/MpuDUAvp+08fwNl1sKiISD5VnkQTTdUcXvff1svwry0mvTPc6jiS4kneUEKwMsv/6/YSHNHxDRORgVJ5FEkhoKMSzH3+WnGNzqPqIPi6Xw+cL+qj4QAXhoTDtN7bjnPM6kojIoqbyLJJA9l6xl4l9E9R9vw5fQP/5ytzIqMmgZGMJQ38fYuCvA17HERFZ1PTbVyRBDD8zTMu3W6j4QAX5r9CczjK3Ct9QSOaaTDr+p4OJzgmv44iILFqabUPEY/HcPc45R8s3W7B0I3td9pzecU4EwHxGxUUV7P3yXvb/935qPl2D+XX3QRGR6XTmWSQBDD48yOizo5S8rQR/jt/rOJKk0orSKH9POWO7x+i+p9vrOCIii5LKs8giFx4N03l7JxnLM8g/WcM1ZH7lHp9L7km59Nzdw+iuUa/jiIgsOirPIotcz909hAfDlJ1bhvn0MbrMv7JNZQSKAuz/0X4iYxGv44iILCoqzyKL2ET7BL2/7SXvlXlkLMvwOo6kCH+mn8r3VzLZPUnHrR1exxERWVTiKs9mdoaZ7TCzBjO7bIb1Zmbfia1/0szWT1vvN7O/m9ldcxVcJBV0/rwTX5qPkrfqFtyysDJXZVL0xiIG/jLA0FNDXscREVk0DlqezcwPXA2cCawDzjWzddM2OxOoi31tBq6Ztv7jQP1hpxVJIcNPDzP81DBFZxcRyNPEOLLwis4uIlgVpP2n7YSHdfdBERGI78zzCUCDc67ROTcB3AJsnLbNRuAGF/UQUGBmlQBmtgQ4C7huDnOLJDUXcnTc2kFaeRqFry30Oo6kKF+aj4r3VRAeDGv4hohITDzluRponvK8JbYs3m3+C/gM8JJXnZjZZjPbamZbOzs744glkrz6ftfHZPskpe8qxQK6SFC8k7E0g6Izixh8aJChxzV8Q0QknvI8029uF882ZnY20OGce/Rgb+Kc2+Kc2+Cc21BaWhpHLJHkFBoI0X1XN1lHZpFzVI7XcUQoflMx6TXptN/YTnhIwzdEJLXFU55bgJopz5cA029vdqBtTgbeYmZ7iA73eJ2Z3XjIaUVSQNcvu4hMRCh7V5nXUUQAsED07oPhkTAdP9PwDRFJbfGU50eAOjNbbmZBYBNw57Rt7gQuiM26cRLQ75xrc85d7pxb4pyrjb3ut8658+byBxBJJuMt4wz8ZYCC1xYQrAh6HUfkeelL0ik+u5jBrYMMPjrodRwREc8ctDw750LApcB9RGfMuNU594yZXWxmF8c2uwdoBBqAHwIfmae8Ikmt8/ZOfJk+is8q9jqKyIsUvbGI9KXpdNzSodk3RCRlxTX/lXPuHqIFeeqya6c8dsAlB9nH74HfzzqhSIoYfmaYkW0jlL6rFH+23+s4Ii9ifqP8gnKavtpE522dVFxY4XUkEZEFpzsMiiwCLuLovK2TtJI08k/J9zqOyAFl1GRQeHohA38dYLh+2Os4IiILTuVZZBEY+OsAE60TlLy9BF+a/rOUxa34rGLSytLouLGD8IiGb4hIatFvaRGPRcYidP2yi4yVGeSs19R0svj5gj7Kzy9nsmuS3f+62+s4IiILSuVZxGM99/cQHghT+s5SzHRDFEkMWauzyH9NPi3fbmHgkQGv44iILBiVZxEPje8bp/f+XnJenkPmikyv44jMSsnbSwhWBNnxwR1EJl/yJrIiIklD5VnEQ7u/uBsclLytxOsoIrPmz/Sz+vurGX5ymJZvtXgdR0RkQag8i3hkeNsw+3+8n/xT8gmW6oYokphKNpZQvLGYPVfsYaxpzOs4IiLzTuVZxCONn23En+On+E26IYoktrqr6gBo+ESDx0lEROafyrOIB/r/0k/3L7tZ+pml+HN0QxRJbBnLMqj911q6ftFF993dXscREZlXcd1hUETmjnOOXf+yi2BFkCWfWEL7Te1eRxI5ZK1bWgHw5/oJVgbZftF2ln1xGb7g7M/NVG2umut4IiJzTmeeRRZY913dDPxlgNp/q9VtuCVpWMAoO7eMya5Jeu7t8TqOiMi8UXkWWUAu7Gi8rJHM1ZlUvL/C6zgicyprTRa5J+bSe18vE+0TXscREZkXKs8iC2j/DfsZ2TbCiq+u0G24JSmVvqMUSzM6ftaBc87rOCIic06/vUUWSHg0zJ5/3UPuCbmUvF3zOktyCuQHKNlYwkj9CEOPDnkdR0Rkzqk8iyyQ1u+3Mt4yzopvrNBtuCWp5Z+ST/rSdDpu7SA8GvY6jojInFJ5FlkAoYEQe7+2l8I3FlJ4aqHXcUTmlfmMsveUER4I0/0rTV0nIslF5VlkAbR8u4VQd4gVV67wOorIgshcnkn+q/Pp+20fY82686CIJA+VZ5F5NtE1QfM3myl5Rwm5L8/1Oo7Igil5awn+HD8dN3fgIrp4UESSg8qzyDxr/kYz4eEwy69Y7nUUkQXlz/ZT+o5SxhrHGPjrgNdxRETmhMqzyDwa3zfOvu/to/z8crLXZXsdR2TB5Z6US+aqTDrv6CQ8pIsHRSTxqTyLzKO9X9mLCztqv1jrdRQRT5hFLx6MjEbovKPT6zgiIodN5VlknozuGqXtujYqN1eSuTzT6zginkmvTqfw9YUM/GWA0V2jXscRETksKs8i82T3F3djacayzy3zOoqI54rPKiZQGIjeeTCsiwdFJHGpPIvMg6Gnhui4uYPqj1WTXpnudRwRz/kyfJS+q5Tx5nH6/tDndRwRkUOm8iwyD3Z/YTf+XD9LP7PU6ygii0bO+hyy1mXR/ctuQv0hr+OIiBwSlWeROTbw8ADdv+ym5tM1pBWleR1HZNEwM8o2leFCjs7bdPGgiCQmlWeROdb4uUbSStNY8vElXkcRWXSC5UEK31jI4N8GGdkx4nUcEZFZU3kWmUO9v+ml7zd9LP3sUgK5Aa/jiCxKRWcUkVaSFr3zYEgXD4pIYonrt7uZnQFcBfiB65xzX5+23mLr3wSMABc55x4zswzgj0B67L1uc859cQ7ziyy41i2tMy53ztH8jWYChQEszQ64nUiq8wV9lG4qpfV7rfT+upeiM4q8jiQiEreDnnk2Mz9wNXAmsA4418zWTdvsTKAu9rUZuCa2fBx4nXPuGOBY4AwzO2luoossLsNPDjO2e4zis4vxpelDHZGXknNUDtnHZtN9dzeTPZNexxERiVs8v+FPABqcc43OuQngFmDjtG02Aje4qIeAAjOrjD0fim2TFvvSZ3SSdFzE0fW/XaSVpZH3ijyv44gkhLJzysBB5//o4kERSRzxlOdqoHnK85bYsri2MTO/mT0OdAAPOOcePuS0IovU4NZBJlonKH5LMeY3r+OIJIS04jSKzy5m6PEhhp4aOvgLREQWgXjK80xNYPrZ4wNu45wLO+eOBZYAJ5jZkTO+idlmM9tqZls7O3UWQhKHCzu67+wmuCRI7stzvY4jklAKX19IsCJI5y2dhEfDXscRETmoeMpzC1Az5fkSYPqVUAfdxjnXB/weOGOmN3HObXHObXDObSgtLY0jlsji0P/nfiY7JynZWIL5dNZZZDYsYJSdW8Zk1yRN32jyOo6IyEHFU54fAerMbLmZBYFNwJ3TtrkTuMCiTgL6nXNtZlZqZgUAZpYJvB7YPnfxRbwVmYjQfXc3GSszyD4q2+s4Igkp64gsco/PpenrTYw0aO5nEVncDlqenXMh4FLgPqAeuNU594yZXWxmF8c2uwdoBBqAHwIfiS2vBH5nZk8SLeEPOOfumuOfQcQzfb/rI9wfpuRtJURnbBSRQ1H6rlJ8QR/PXvoszum6chFZvOKa59k5dw/Rgjx12bVTHjvgkhle9yRw3GFmFFmUwiNheu7tIevILLLqsryOI5LQAvkBln95OQ2faKDz9k7K3lnmdSQRkRlpMlqRQ9R7fy+RkQglG0u8jiKSFKouqSLnuBwaPtrAZJ/mfhaRxUnlWeQQhPpD9P6ml9zjc8lYmuF1HJGk4Av4WHPdGiY6J2j8TKPXcUREZqTyLHIIeu7pwYUcxW8u9jqKSFLJXZ9LzT/X0PbDNvr+0Od1HBGRF1F5Fpmlya5J+v7UR/7J+QTLg17HEUk6tf9WS8aKDHZ8aAfhMc39LCKLi8qzyCx1/6ob8xlFZxV5HUUkKfmz/KzZsobRZ0fZ++W9XscREXkBlWeRWRh6eoiBhwcoOLWAtMI0r+OIJK3C0wqpuKiC5n9vZugJ3bpbRBYPlWeRWdj9+d340n0UnaGzziLzbeV/riRQFGDHB3fgwpr7WUQWB5VnkTj1P9RP9y+7KTy9EH+O3+s4IkkvrTiNuu/UMbh1kOZvNnsdR0QEUHkWiYtzjt2f3U1aWRqFpxV6HUckZZSeU0rJ20vY/YXdDD8z7HUcERGVZ5F49P66l77f9bHsc8vwZeg/G5GFYmasvmY1gbwA9RfWE5mMeB1JRFKcWoDIQTjnaLy8kfRl6VR9uMrrOCIpJ1gWZPW1qxl6dIimrzd5HUdEUpzKs8hBdN7eydCjQyz/0nJ86fpPRsQLpe8opWxTGXuv2Mvg44NexxGRFKYmIPISIqEIuz+/m6x1WZSfV+51HJGUVve9OtJK0th+4XYiExq+ISLeUHkWeQntN7QzumOU5V9ZjvnN6zgiKS2tOI3VW1Yz/OQwe67Y43UcEUlRKs8iBxAeCbP7C7vJPTGXkreWeB1HRICSN5dQfmE5TV9rov+v/V7HEZEUpPIscgAt325honWClf+5EjOddRZZLOquqiNjaQb1760n1B/yOo6IpBiVZ5EZTHRM0PSNJkreWkLBqwq8jiMiUwTyA6y9aS1jzWPsvGSn13FEJMWoPIvMYM+X9hAeCbPi6yu8jiIiM8h/ZT61X6il46YO2m9q9zqOiKQQlWeRaUZ2jND6g1aqPlxF1posr+OIyAEs/dxS8k7OY+f/28lo46jXcUQkRag8i0zTeHkj/iw/tV+s9TqKiLwEX8DH2hvXgkH9efVEQpq+TkTmn8qzyBR9f+6j6xddLP2XpQTLgl7HEZGDyKzNZPW1qxl4cIC9X97rdRwRSQEqzyIxzjkaP91IsCrIkn9a4nUcEYlT+bnllF9Qzt4v76X3t71exxGRJKfyLBLT+fNOBh4aYPmXl+PP8nsdR0Rmoe7qOrLWZLHtPdsY3z/udRwRSWIBrwOILAbh0TC7Pr2L7GOyqbiwwus4IimpdUvrYb2+9JxSmr7WxOOnPs6STyyh+uLqOUomIvIPOvMsAjR/s5nxpnHqrqrTbbhFElR6dTpl55YxumOU7ru6vY4jIklK5VlS3vi+cZq+1kTpO0spOKXA6zgichjyT84n7xV59NzTQ88DPV7HEZEkpPIsKa/xskZc2LHiP3RDFJFkUHZuGcGKIPXvrWe8VeOfRWRuacyzpIQDjaUcbRyl/cZ2is4sovf+XnrRlfoiic6X7qNycyXN/9HMtk3bOOY3x+BL07kiEZkb+tdEUpaLODpv7cSf76fojCKv44jIHEqvSmfNdWvo/1M/uz69y+s4IpJE4irPZnaGme0wswYzu2yG9WZm34mtf9LM1seW15jZ78ys3syeMbOPz/UPIHKoBv82yNjuMUreVoIvQ39HiiSb8nPLqf54Nfuu2kf7z9q9jiMiSeKgjcHM/MDVwJnAOuBcM1s3bbMzgbrY12bgmtjyEPBJ59xa4CTgkhleK7LgImMRun7RRXptOnkn5nkdR0Tmycr/WEn+q/PZ8cEdDD015HUcEUkC8ZxuOwFocM41OucmgFuAjdO22Qjc4KIeAgrMrNI51+acewzAOTcI1AOaeFM81313N6G+EGXnlGE+TU0nkqx8aT7W3bqOQH6Ap9/2NJN9k15HEpEEF095rgaapzxv4cUF+KDbmFktcBzw8KxTisyh8dZxen/dS97JeWSuzPQ6jojMs/SKdF7285cxvnec7edvx0Wc15FEJIHFU55nOi03/V+el9zGzHKA24FPOOcGZnwTs81mttXMtnZ2dsYRS2T2nHN03NyBL9NH6dtLvY4jIgsk/+R8Vn5rJd13dbPnij1exxGRBBZPeW4BaqY8XwJMn/frgNuYWRrR4nyTc+6OA72Jc26Lc26Dc25DaalKjcyPwYcHGX12lJK3luDP8XsdR0QWUPWl1ZRfWM7eL+2l83adpBGRQxNPeX4EqDOz5WYWBDYBd07b5k7ggtisGycB/c65NjMz4L+Beufct+Y0ucgshUfCdN7WSUZtBvmvyvc6jogsMDNj9bWryTspj/oL6hl6QhcQisjsHbQ8O+dCwKXAfUQv+LvVOfeMmV1sZhfHNrsHaAQagB8CH4ktPxk4H3idmT0e+3rTXP8QIvHo/mU34aEwZe/VRYIiqcqf4edld7yMQGGAp97yFBMdE15HEpEEE9cdBp1z9xAtyFOXXTvlsQMumeF1f2bm8dAiC2qsaYy+P/RRcEoBGUszvI4jIh5Kr0znyP89ksdf/TjPvPMZjvn1MfiCmutdROKj23NL0nNhR8dNHfhz/RRvLPY6jogskNYt0y/PeaGy88rYf91+njjtCcrOKyM60nBmVZur5jqeiCQo/aktSa/luy2M7Rmj9F2l+LN0kaCIROUdn0fRmUX0/7mf/t/3ex1HRBKEyrMktdHdo+z+3G6yj8om9/hcr+OIyCJT/JZiso/OpuPWDka2j3gdR0QSgMqzJC3nHDs378T8Rtl7XvojWRFJTeYzKt5fQbAiSOuWViY6dQGhiLw0lWdJWvuv30/vr3tZ8e8rSCtK8zqOiCxS/kw/VR+JjmluvbqV8GjY40QispipPEtSGm8bZ9c/7yL/Nfm60EdEDipYGqRqcxUT7RPs/9F+3cJbRA5I5VmS0rOXPktkLMKaH67RnM4iEpesI7IoO6eM4SeH6fpll9dxRGSR0lR1knQ6b++k644uVnxjBVmrs7yOIyIJJP/UfMZbx+m9t5f0inTyXpHndSQRWWRUniWpTLRPsPP/7SRnfQ5L/nmJ13FEJMGYGWWbypjsmGT/T/cTKA7oj3AReQEN25Ck4Zxj+we2Ex4Ms/ana/EFdHiLyOyZ36j8cCXB0iCt17Qy0a4ZOETkH9QuJGm0bWmj5+4eVnxjBdnrsr2OIyIJzJ/lp+rSKsxv7PvePia7J72OJCKLhMqzJIWRnSM0/HMDhW8opPrSaq/jiEgSCJYGqfp/VYR6Qjz9tqeJjEe8jiQii4DKsyS8yGSE+vPr8aX7OOLHR2h2DRGZM5krMym/qJz+P/Wz44M7cE5T2ImkOl0wKAlv75V7GfzbIOtuXUd6dbrXcUQkyeQdn0dmbSa7P7+b9GXprPjKCq8jiYiHVJ4lofU/1M/er+yl/Pxyyt5V5nUcEUlSSz+7lLG9YzRd2URGTQZVH9bNl0RSlcqzJKzJnkm2vXsbGTUZ1H23zus4IpLEzIy679cxvm+cnR/ZSbA6SMnZJV7HEhEPaMyzJCQXcdRfUM/E/gnW3bqOQL7+DhSR+eUL+Fj3P+vIXZ/LtndvY+CRAa8jiYgHVJ4lITX9exM9d/ew6luryDtedwATkYURyAlw1F1HESwP8tRZTzG6a9TrSCKywHS6Thal1i2tB1w3snOElm+1kHt8LgReelsRkbkWLA9y9L1H89grH+OJNz7B+r+sJ1ge9DqWiCwQnXmWhBIaCNH2wzbSytIoP68cM01LJyILL2t1FkfffTQT+yd44o1PEOoPeR1JRBaIyrMkDBdxtF3XRmQ0QtWHq/Bl6PAVEe/knZjHkXccyci2EZ56y1OER8NeRxKRBaD2IQmj644uRneMUvaeMs3nLCKLQtHpRaz96Vr6/9TPtk3biIR0F0KRZKfyLAmh/8/99D7QS8FrC8h/Zb7XcUREnlf27jLqvldH953d0bsQRnQXQpFkpgsGZdEb2TlC+83tZK3LovRdpV7HEZEUdLALky1gFJ9dTPtP2hlvHqf0nNKXvCajarNusiKSqFSeZVGb6Jyg9dpW0krSqPxQJebXBYIisjgVnV1EeDRM32/6sIBR8vYSXdQskoRUnmXRCo+Gab26FRxUX1KNP8vvdSQRkQMyM0rfVYoLOXrv78XSjJK36C6EIslG5VkWJRd27L9uPxPtEyz5xBLNoSoiCcHMKNtUhgs7eu7uiQ7neFOx17FEZA6pPMui45yj/aZ2hp8epuy9ZWStyfI6kohI3MxnlL+3HBdydP+yGwsYRacXeR1LROaIyrMsOo2XNzLwlwGKziqi4DUFXscREZk18xkVF1TgJh1dt3eBg6I3qkCLJIO4pqozszPMbIeZNZjZZTOsNzP7Tmz9k2a2fsq6H5lZh5k9PZfBJTk1f7OZ5m80k39KPsVv1kedIpK4zG9UfqCS3ONz6bqji65fdeGcprETSXQHLc9m5geuBs4E1gHnmtm6aZudCdTFvjYD10xZdz1wxlyEleS2/yf72fWpXZSeU0rZpjJdpS4iCc/8RsX7K8h7ZR49d/XQdYcKtEiii+fM8wlAg3Ou0Tk3AdwCbJy2zUbgBhf1EFBgZpUAzrk/Aj1zGVqST9ddXWz/wHYKX1/I2hvWYj4VZxFJDuYzys8vJ//UfHrv76Xjlg7dSEUkgcVTnquB5inPW2LLZruNyIy67+3mmXc+Q+5xubzsjpfhS9eNL0UkuZgvOgtH4emF9P++nx0f2KFbeYskqHguGJzpFOD0P5nj2eal38RsM9EhHyxdunQ2L5UE1n1vN0+/9Wmy12Vz9H1HE8jVNawikpzMojdO8QV97L8+OhXnulvXEcjRv3siiSSeU3wtQM2U50uA6fcpjWebl+Sc2+Kc2+Cc21Baqlswp4Lu/+vm6Y3R4nzMr48hrSjN60giIvPKzCh+czGrf7Canvt7ePyUxxlvG/c6lojMQjzl+RGgzsyWm1kQ2ATcOW2bO4ELYrNunAT0O+fa5jirJJHue2JnnI9UcRaR1FO1uYqj7jyKkR0jPPaKxxjeNux1JBGJ00HLs3MuBFwK3AfUA7c6554xs4vN7OLYZvcAjUAD8EPgI8+93sx+BjwIrDGzFjP7wBz/DJJguu/u5um3xYrzAyrOIpKait9UzHF/OI7IWIS/n/x3en/f63UkEYmDLcYpczZs2OC2bt3qdQyZB+03tbP9ou1kHxMrzoUzF+fWLbMa9SMiklCqNlc9/3h0zyhPnfkUow2jrPzPlVR/rFpTdYosEDN71Dm3YTav0bQGsmBarmqh/rx68l+dz7G/PfaAxVlEJJVk1may/qH1FJ1VRMMnGqg/r57wcNjrWCJyACrPMu+cczR+rpGGTzRQ8vYSjrrnKAJ5urpcROQ5gfwAR95xJMuvXE7Hzzp47BWPMdIw4nUsEZmByrPMKxd27Lx4J01fbaLyg5W87NaX4c/wex1LRGTRMZ+x7LPLOPreoxnfN86jGx6l8/ZOr2OJyDQqzzJvQkMhnn7707RtaWPp5UtZvWU15tc4PhGRl1J0ehEvf/TlZNVl8cw7n6H+gnom+ya9jiUiMfrsXObM1Iv8Jnsnab26lfGWcUo3lZJRm0HbDzV7oYhIPDJrMznur8ex9yt72XvlXvp+18eaH62h6A1FXkcTSXk68yxzbqxpjKavNzHRMUHVJVUUvrbQ60giIgnHl+Zj+ZeWs/7B9fhz/Dx5+pPsvHQnocGQ19FEUprKs8ypoSeGaP7PZsyMpZ9ZSs5ROV5HEhFJaHnH5/Hyx17Okn9aQuv3W/nbmr+x/6f7cZHFN9WsSCpQeZY54Zyj574eWq9pJVgeZOnlS0lfku51LBGRpODP9LPqW6tY/+B60mvS2X7Bdv5+8t8ZeGTA62giKUflWQ5beDjMtk3b6Lqji5z1OdR8qoZAvobTi4jMtbwT81j/4HqOuP4IRneP8tgJj7H9fdsZ3T3qdTSRlKHyLIdltHGUx17xGJ23dVLy9hIqP1SJL12HlYjIfDGfUXFhBSfuPJGaT9fQfnM7D9c9TP1F9Yzs0NzQIvNNpwflkPXc38O2TdsAOPr/jmZsz5jHiUREEsPU2YkOR+aqTE5qPInm/2ym9QettN/QTum7Sll6+VJyj82dk/cQkRfSKUKZNRd27PnSHp4840nSl6Tz8kdeTtHpmj5JRMQL6dXprPr2Kk7acxJL/2UpPf/Xw6PHPcqjJz1K23+3ERrS7Bwic0nlWWZlfP84T5z+BHv+bQ/l55Wz/sH1ZK7M9DqWiEjKC5YFWfG1FZy09yRWfnsl4YEwOz64gwerHmTHxTvof6hfM3SIzAEN25C49f6ul/r31BPqD7HmR2uouKgCM90xUERkMUkrTKPmEzUs+fgSBv46QOuWVtp/0k7bD9oIVgcpfXsppe8oJf9V+brrq8ghUHmWg4qEIjRd2cSeK/aQtTqLox84mpwjNX+ziMhiZmbkn5xP/sn5rLpqFd2/6qbz9k7aftjGvu/uI60sjaIziyg6vYjC1xcSLAt6HVkkIag8y0saeXaE+vPrGXx4kPLzy6n7fh2BHB02IiKJJK0gjYrzK6g4v4LQUIie/+uh644uun/VTftP2gHIOS6HwjcUUvDaAvJPzieQq3/rRWZizi2+8U8bNmxwW7du9TpG0nupq72dc/T/qZ/On3diAaP8PeXkHq8rt0VEkomLOMabxhneNsxI/QijDaMQAfyQuz6X/NfkU3BKAfmvyietMM3ruCJzzswedc5tmM1r9GelvEhoIET7De0MPzVM1tosyi8s1z+aIiJJyHxGRm0GGbUZFL+pmMh4hKy1WfT/oZ++P/Sx77v7aPlmCxhkH51NwSkF0TL9mnyCJRrmIalJZ55T2PQzz845Bv4yQOftnbhJR8nbSyg4tQDz6YISEZFUFJmMMLZ7jNGdo4w+O8rorlHcZLQ3BCuDZK7OJKsui8zVmTPeWbZqc9VCRxaZFZ15lkM2sX+C9pvaGd05SmZdJuXnlROs0FkFEZFU5kvzkbU6i6zVWQC4kGNsb7RMjzw7wsBDA/T/oR+AtLK0F5TptCJ9YinJSeU5xUUmI/Te30vPPT1Y0Cg/v5y8V+bpbLOIiLyIBYzMlZlkrsyk6MwiXNgx3jzOyM4RRp8dZeixIQb+PABAoDhA/1/7KXhNdKhHxooMTW8qSUHlOUU55xh8dJCuO7qY7Jokd0MupeeUzvixm4iIyEzM/48x05weuwBx3/jzwzx67u55fjaPYHWQglMKKDytkMI3FJJRk+FxepFDo6aUgvof7GfXJ3cx8OAAweog1R+vJntdttexREQkwZnPyKjJIKMmg8LTCqn8YCUj9SP0/bGPvj/00fubXjpu7gAgc01mdI7pNxRScGqBpsaThKELBlPI8DPD7LliD523dhKsCFL4hkIN0RARkQXjnGOidYKRbSMM1w8zujN2AaIPMldkkrU2i6y1WWTUZsx490NdgChzTRcMyowGHh5g79f20v3LbnxZPpb96zJqPl3z/F//IiIiC8HMSK9OJ706ncI3FEZn89g1xnD9MCPbRui+q5vuX3Xjy/SRuSaT7HXZZK3NIq00TeOlZdFQeU5SLuLofaCXpm800fe7PgKFAZZ9cRnVl1Zrbk4REVkUfGk+so7IIuuILHgbhIfCjGyPnpUeqR9h+PFhIHrxYdYRWVjQKHhNARnLdfGheEflOcmM7R1j//X7aftxG+N7xwlWBVn5zZVUbq7UbbVFRGRR8+f4yd2QS+6GXJxzTHZMRkt0/TBDjw+x4307AAhWBSl4TQF5J+WRe0IuOcfm4M/0e5xeUoXaVBKY6Jqg554e2n/aTu9vegEofH0hK76+gtK3leJL93mcUEREZHbMjGB5kGB5kIJTC3ARR/6r8un/Uz/9f+yn7099dNwSHX5oASP7qGxyj88l55gcso/MJvvIbM01LfNC5TkBOecYfmb4+bFhAw8OgIOM2gxq/62WigsryFimKYBERCR5mM/IOTKHnCNzqP5/1QCM7xtn4JEBBh8ZZPBvg3Te2knblrbnXxOsDJL9smwyVmY8Pz915spMMpZn4M/1a+iHHJK4yrOZnQFcBfiB65xzX5+23mLr3wSMABc55x6L57VycOHhMINbB+l/sJ+BhwYYeHCAyY5JAHJensOyf11G8dnF5K7P1cwZIiKSMtKr0ymtLqX0raVA9OTS+L5xhp8ejn49FR073fnzTkI9oRe81pftI70qnWBVkPSqdNLK0kgrSiNQFIh+Lwzgz/bjy/Lhz/Tjy/S94LGl2QvKd+uW1jn7uTSryOJ20PJsZn7gauANQAvwiJnd6ZzbNmWzM4G62NeJwDXAiXG+VoDIRISJtgnGmmO3Pd0+8vzX6K5RiES3y6zLpOiMIvJflU/xm4pJr073NriIiMgiYWZkLMkgY0kGxWcUv2DdZO8ko7tGGds1xljTGBOtE4y3jjPRNsHAwwNMdk0SHgjH/2Z+okU6I1qkI+MRzG/RKfb8PP/Y/AY+DrxuhufjzeNYwLA0w5fuixb32NfzRT7Thz/LP+O66cVe5lY8Z55PABqcc40AZnYLsBGYWoA3Aje46KTRD5lZgZlVArVxvHZRcs6Bi85aQeQg38MQGYsQHg0TGYsQGY3M+D00ECLUG/2a7J0k1Btiom2C8X3jz59Jfo4FjazVWeQck0PZpjLyTswj76Q80oo1fktERFLTXJ3dDeQHCOQHyFqb9YLlLuwIj4SJDEcID4eJTERwEw436aKPJx1u4h+PIxMRXMjhwtEu4MLu+a+pzwlDZDISfR558brpr+t9oPf5k2aHxMcBi/ULzqTHuy7rxcXdl+mLln2LDqmZ+h3fAZaZ/eN7AounPFcDzVOetxA9u3ywbarjfO2i8MQbn6Dvd33Pl2Lm694xFvuPtihAoDBAsCpI7vG50Y+OqoOkV6eTtfrAE8SLiIjI/DC/Re90mOttjqrNVbjIP0p7ZDT6FR4NRx+PTHk8dd3ItO1GI9E/BqZtF+oNzbgPF1rAG+dNK9S1X6xl2WeXLdz7H4Z4yvNMDW76/7oH2iae10Z3YLYZ2Bx7OmRmO+LI9pwSoGsW23vHAX2xL5kPiXMsyELQ8SDP0bEgz1n8x8KHvQ6wABwwdZTM52JfC2/NbF8QT3luAWqmPF8CTP/c5EDbBON4LQDOuS3AljjyvIiZbZ3trRUlOelYkKl0PMhzdCzIc3QsyFRmtnW2r4lnAuBHgDozW25mQWATcOe0be4ELrCok4B+51xbnK8VEREREUkIBz3z7JwLmdmlwH1Ep5v7kXPuGTO7OLb+WuAeotPUNRCdqu59L/XaeflJRERERETmWVzzPDvn7iFakKcuu3bKYwdcEu9r58EhDfeQpKRjQabS8SDP0bEgz9GxIFPN+niwaO8VEREREZGDiWfMs4iIiIiIkIDl2cx+ZGYdZvb0lGVFZvaAmT0b+17oZUZZGGZWY2a/M7N6M3vGzD4eW67jIcWYWYaZ/c3MnogdC1+KLdexkKLMzG9mfzezu2LPdSykKDPbY2ZPmdnjz82soOMhNcVu4nebmW2PdYdXHMqxkHDlGbgeOGPassuA3zjn6oDfxJ5L8gsBn3TOrQVOAi4xs3XoeEhF48DrnHPHAMcCZ8Rm/tGxkLo+DtRPea5jIbW91jl37JQp6nQ8pKargHudc0cAxxD9N2LWx0LClWfn3B+BnmmLNwI/iT3+CfDWhcwk3nDOtTnnHos9HiT6H0E1Oh5Sjosaij1Ni305dCykJDNbApwFXDdlsY4FmUrHQ4oxszzgNcB/AzjnJpxzfRzCsZBw5fkAymPzShP7XuZxHllgZlYLHAc8jI6HlBT7mP5xoAN4wDmnYyF1/RfwGSAyZZmOhdTlgPvN7NHY3YxBx0MqWgF0Aj+ODem6zsyyOYRjIVnKs6QwM8sBbgc+4Zwb8DqPeMM5F3bOHUv0TqYnmNmRHkcSD5jZ2UCHc+5Rr7PIonGyc249cCbR4X2v8TqQeCIArAeucc4dBwxziMN1kqU8t5tZJUDse4fHeWSBmFka0eJ8k3PujthiHQ8pLPYx3O+JXhuhYyH1nAy8xcz2ALcArzOzG9GxkLKcc62x7x3AL4AT0PGQilqAltinkgC3ES3Tsz4WkqU83wlcGHt8IfBLD7PIAjEzIzp2qd45960pq3Q8pBgzKzWzgtjjTOD1wHZ0LKQc59zlzrklzrlaYBPwW+fceehYSElmlm1muc89Bk4HnkbHQ8pxzu0Hms1sTWzRacA2DuFYSLibpJjZz4BTgRKgHfgi8L/ArcBSoAl4l3Nu+kWFkmTM7FXAn4Cn+MfYxs8SHfes4yGFmNnRRC/08BM9KXCrc+4KMytGx0LKMrNTgU85587WsZCazGwF0bPNEP3Y/mbn3JU6HlKTmR1L9ELiINAIvI/Y7wxmcSwkXHkWEREREfFKsgzbEBERERGZdyrPIiIiIiJxUnkWEREREYmTyrOIiIiISJxUnkVERERE4qTyLCKSAMys1syePsTXVpnZbXOdSUQkFQW8DiAiIvMrdoe1d3qdQ0QkGejMs4hI4giY2U/M7Ekzu83Mssxsj5l91cweNLOtZrbezO4zs11mdjEc3llrERF5IZVnEZHEsQbY4pw7GhgAPhJb3uycewXRO25eT/Qs80nAFV6EFBFJZhq2ISKSOJqdc3+JPb4R+Fjs8Z2x708BOc65QWDQzMbMrGCBM4qIJDWdeRYRSRzuAM/HY98jUx4/91wnSURE5pDKs4hI4lhqZq+IPT4X+LOXYUREUpHKs4hI4qgHLjSzJ4Ei4BqP84iIpBxzbvqngCIiIiIiMhOdeRYRERERiZPKs4iIiIhInFSeRURERETipPIsIiIiIhInlWcRERERkTipPIuIiIiIxEnlWUREREQkTirPIiIiIiJx+v8NZTw7DityYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.title(\"Distribution of bmi\")\n",
    "ax = sns.distplot(df['bmi']**2, color = 'm') # squaring because we applied square transformation in EDA stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The average BMI in patients is 30. With a value equal to 30 starts obesity\n",
    "- lets check charges to people who smoke and also have BMI more than 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23298f62eb0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 620.5x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 =df.copy()\n",
    "df2['bmi']= df2['bmi']**2\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.lmplot(x='bmi', y=\"charges\", hue=\"smoker\", data=df2, palette = 'magma', height = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As expected people who smoke and also are obese have high charges; with increase in BMI greately increse charges\n",
    "- Lastly see children i.e dependent on insurance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.9 % people have 0 children\n",
      "24.22 % people have 1 children\n",
      "17.94 % people have 2 children\n",
      "11.73 % people have 3 children\n",
      "1.87 % people have 4 children\n",
      "1.35 % people have 5 children\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"children\", kind=\"count\", palette=\"magma\", data=df, height = 6)\n",
    "for i in (0,1,2,3,4,5):\n",
    "    print(round(sum(df['children'] == i)/len(df)*100,2),\"%\" + \" people have\", i, \"children\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2329a16d8b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 620.5x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.lmplot(x='children', y=\"charges\", hue=\"smoker\", data=df, palette = 'magma', height = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is pretty clear that smokers have high charges and people who have more than 1 child get high charges "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Implementation\n",
    "1. Polynomial Feature, target transformation and standardscaler \n",
    "2. Linear regression\n",
    "2. Linear ridge regression \n",
    "3. Linear Lasso regression\n",
    "4. Linear Elastic Net Regression \n",
    "5. RandomForest regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import (PolynomialFeatures,StandardScaler, \n",
    "                                   PolynomialFeatures)\n",
    "from sklearn.metrics import r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- split the dataset and transform target by box-cox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y, X = df['charges'], df.drop(columns='charges')\n",
    "quad = PolynomialFeatures (degree = 2)\n",
    "x_quad = quad.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_quad, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.061262941323214304"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_result2 = boxcox(y_train)\n",
    "y_train_bc = bc_result2[0]\n",
    "lam2 = bc_result2[1]\n",
    "lam2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. linear regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- applying standard scaling\n",
    "- train linear regression model\n",
    "- make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "s = StandardScaler()\n",
    "X_train_s = s.fit_transform(X_train)\n",
    "X_test_s= s.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.02067015e-17  1.18609319e+00 -2.60372971e-01  6.99345112e-01\n",
      "  5.85427529e-01 -1.77635694e-01  1.01322218e-01  2.42557107e-02\n",
      "  7.99133375e-03 -2.68593737e-01  1.58874832e-01 -2.28262058e-02\n",
      " -3.55701246e-01 -8.96401494e-01  1.04987984e-01  2.42894813e-01\n",
      "  2.82945675e-01 -2.60372971e-01  2.47308263e-01 -6.42240292e-03\n",
      "  4.76285719e-02  7.60714612e-02  6.33377224e-02  1.26133527e-03\n",
      " -6.43333556e-01  1.11279704e-01  2.33696229e+00 -4.02250796e-01\n",
      " -4.32444583e-01 -3.92235958e-01 -9.61798066e-02 -1.43921923e-01\n",
      " -9.57493213e-03 -1.39636102e-02 -6.20042098e-02 -1.77635694e-01\n",
      "  1.33483694e-02  2.64661264e-02  7.80290503e-02  1.01322218e-01\n",
      "  0.00000000e+00  0.00000000e+00  2.42557107e-02  0.00000000e+00\n",
      "  7.99133375e-03]\n"
     ]
    }
   ],
   "source": [
    "lr=lr.fit(X_train_s, y_train_bc)\n",
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_bc = lr.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Take inverse target transform as we want predictions in normal comparable units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8548992593040151"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import inv_boxcox\n",
    "y_pred_tran = inv_boxcox(y_pred_bc,lam2)\n",
    "r2_score(y_pred_tran,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- linear regression without target transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8368610554382401"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train_s,y_train)\n",
    "lr_pred = lr.predict(X_test_s)\n",
    "r2_score(lr_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now I can say the target transformation is helping the model to learn \n",
    "- lets plot the predictions; values on diagonal line are predicted with high accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(6,6))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(y_test,y_pred_tran, \n",
    "         marker='o', ls='', ms=3.0)\n",
    "\n",
    "lim = (0, y_test.max())\n",
    "\n",
    "ax.set(xlabel='Actual Price', \n",
    "       ylabel='Predicted Price', \n",
    "       xlim=lim,\n",
    "       ylim=lim,\n",
    "       title='Linear Regression Results on validation data');\n",
    "\n",
    "diag_line, = ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n",
    "x_lims = ax.get_xlim()\n",
    "y_lims = ax.get_ylim()\n",
    "diag_line.set_data(x_lims, y_lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Ridge regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use Ridge CV to get best alpha value\n",
    "- train model with the best aplha value\n",
    "- inverse transform the predictions and check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "def rmse(ytrue, ypredicted):\n",
    "    return np.sqrt(mean_squared_error(ytrue, ypredicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.065 4537.838697582912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.06, 0.065]\n",
    "\n",
    "ridgeCV = RidgeCV(alphas=alphas, \n",
    "                  cv=4).fit(X_train, y_train)\n",
    "\n",
    "ridgeCV_rmse = rmse(y_test, ridgeCV.predict(X_test))\n",
    "\n",
    "print(ridgeCV.alpha_, ridgeCV_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = Ridge(alpha=0.065)\n",
    "rr = rr.fit(X_train_s, y_train_bc)\n",
    "Y_pred_rr = rr.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8547912725199049"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import inv_boxcox\n",
    "y_pred_tran = inv_boxcox(Y_pred_rr,lam2)\n",
    "r2_score(y_pred_tran,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3651243061555203"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "np.sqrt(mean_squared_log_error(y_test, y_pred_tran))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- lets plot the predictions; values on diagonal line are predicted with high accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(6,6))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(y_test,y_pred_tran,marker='o', ls='', ms=3.0)\n",
    "\n",
    "lim = (0, y_test.max())\n",
    "\n",
    "ax.set(xlabel='Actual Price', \n",
    "       ylabel='Predicted Price', \n",
    "       xlim=lim,\n",
    "       ylim=lim,\n",
    "       title='Linear Ridge Regression Results on validation data');\n",
    "\n",
    "diag_line, = ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n",
    "x_lims = ax.get_xlim()\n",
    "y_lims = ax.get_ylim()\n",
    "diag_line.set_data(x_lims, y_lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Lasso regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use Lasso CV to get best alpha value\n",
    "- train model with the best aplha value\n",
    "- inverse transform the predictions and check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0005 4537.730558224809\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', module='sklearn')\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas2 = np.array([1e-5, 5e-5, 0.0001, 0.0005])\n",
    "\n",
    "lassoCV = LassoCV(alphas=alphas2,\n",
    "                  max_iter=5e4,\n",
    "                  cv=3).fit(X_train, y_train)\n",
    "\n",
    "lassoCV_rmse = rmse(y_test, lassoCV.predict(X_test))\n",
    "\n",
    "print(lassoCV.alpha_, lassoCV_rmse) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "lassor = Lasso(alpha=0.0005)\n",
    "lassor = lassor.fit(X_train_s, y_train_bc)\n",
    "Y_pred_lr = lassor.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8550587993360111"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import inv_boxcox\n",
    "y_pred_tran = inv_boxcox(Y_pred_lr,lam2)\n",
    "r2_score(y_pred_tran,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36438631163962415"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "np.sqrt(mean_squared_log_error(y_test, y_pred_tran))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of 45 coefficients, 41 are non-zero with Lasso.\n"
     ]
    }
   ],
   "source": [
    "print('Of {} coefficients, {} are non-zero with Lasso.'.format(len(lassoCV.coef_), \n",
    "                                                               len(lassoCV.coef_.nonzero()[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- lets plot the predictions; values on diagonal line are predicted with high accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(6,6))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(y_test,y_pred_tran,marker='o', ls='', ms=3.0)\n",
    "\n",
    "lim = (0, y_test.max())\n",
    "\n",
    "ax.set(xlabel='Actual Price', \n",
    "       ylabel='Predicted Price', \n",
    "       xlim=lim,\n",
    "       ylim=lim,\n",
    "       title='Linear Lasso Regression Results on validation data');\n",
    "\n",
    "diag_line, = ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n",
    "x_lims = ax.get_xlim()\n",
    "y_lims = ax.get_ylim()\n",
    "diag_line.set_data(x_lims, y_lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Elastic Net regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use ElasticNet CV to get best alpha value and L1 ratio\n",
    "- train model with the best values\n",
    "- inverse transform the predictions and check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001 0.4 4537.763891764855\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNetCV\n",
    "\n",
    "l1_ratios = np.linspace(0.1, 0.9, 9)\n",
    "\n",
    "elasticNetCV = ElasticNetCV(alphas=alphas2, \n",
    "                            l1_ratio=l1_ratios,\n",
    "                            max_iter=1e4).fit(X_train, y_train)\n",
    "elasticNetCV_rmse = rmse(y_test, elasticNetCV.predict(X_test))\n",
    "\n",
    "print(elasticNetCV.alpha_, elasticNetCV.l1_ratio_, elasticNetCV_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "EN = ElasticNet(alpha=0.0001, l1_ratio = 0.4 )\n",
    "ElasticNet = EN.fit(X_train_s, y_train_bc)\n",
    "Y_pred_EN = ElasticNet.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8549043250798647"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import inv_boxcox\n",
    "y_pred_tran = inv_boxcox(Y_pred_EN,lam2)\n",
    "r2_score(y_pred_tran,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- lets plot the predictions; values on diagonal line are predicted with high accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(6,6))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(y_test,y_pred_tran,marker='o', ls='', ms=3.0)\n",
    "\n",
    "lim = (0, y_test.max())\n",
    "\n",
    "ax.set(xlabel='Actual Price', \n",
    "       ylabel='Predicted Price', \n",
    "       xlim=lim,\n",
    "       ylim=lim,\n",
    "       title='Linear Elastic Net Regression with polynomial feature Results');\n",
    "\n",
    "diag_line, = ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n",
    "x_lims = ax.get_xlim()\n",
    "y_lims = ax.get_ylim()\n",
    "diag_line.set_data(x_lims, y_lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As expected Lasso model made 4 coefficients zero making very good predictions in linear regression while ElasticNet model predictions were between Ridge and Lasso models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. RandomForest regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- RandomForest is a greedy algorithm and would generally prone to overfitting\n",
    "1. Perform train Test Split with 30% data as a hold out set\n",
    "2. train the model, make predictions\n",
    "3. check mean squared error and R2 error to check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y, X = df['charges'], df.drop(columns='charges')\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_quad, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE train data: 3511593.854, MSE test data: 22318754.619\n",
      "R2 train data: 0.976, R2 test data: 0.848\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "\n",
    "forest = RandomForestRegressor(n_estimators = 100,\n",
    "                              criterion = 'mse',\n",
    "                              random_state = 42,\n",
    "                              n_jobs = -1)\n",
    "forest.fit(X_train,y_train)\n",
    "forest_train_pred = forest.predict(X_train)\n",
    "forest_test_pred = forest.predict(X_test)\n",
    "\n",
    "print('MSE train data: %.3f, MSE test data: %.3f' % (\n",
    "mean_squared_error(y_train,forest_train_pred),\n",
    "mean_squared_error(y_test,forest_test_pred)))\n",
    "print('R2 train data: %.3f, R2 test data: %.3f' % (\n",
    "r2_score(y_train,forest_train_pred),\n",
    "r2_score(y_test,forest_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary \n",
    "\n",
    "- AS Expected RandomForest overfitted on training set thus performed poor on validation set\n",
    "- Lasso model made coefficients zero and performed very good for this data set\n",
    "\n",
    "##### Lasso model predicted more accurately with good varience and Bias balance\n",
    "\n",
    "### Suggestion for next steps\n",
    "\n",
    "- Although the results I got are very good but to improve the results more and find the best model I must revisit the problem and apply more advanced methods such as clustering.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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