Dataset link:https://www.kaggle.com/mirichoi0218/insurance

About the dataset:

age: age of the primary beneficiary

BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, an objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9

children: Number of children covered by health insurance / Number of dependents

smoker: Smoking

region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

charges: Individual medical costs billed by health insurance.

Prerequisites :

• Python 3: https://www.python.org/downloads/
• Anaconda: https://www.anaconda.com/download/

Step -1 :

Import the necessary packages  

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

Short Description about the important libraries :

->Pandas is used to create and manipulate the data frames.

->Numpy is used to create and manipulate the arrays

->Matplotlib is used for visualization purposes.

Data visualization and data preprocessing :

Step 1:

import the dataset from the local folder

data=pd.read_csv(r'D:\Codespeedy\Medical-Insurance-Prediction-master\insurance.csv')
data.head()

Step 2:

data.info()

Output :

Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1338 non-null   int64  
 1   sex       1338 non-null   object 
 2   bmi       1338 non-null   float64
 3   children  1338 non-null   int64  
 4   smoker    1338 non-null   object 
 5   region    1338 non-null   object 
 6   charges   1338 non-null   float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB

Step 3:
Checking the Null values in the dataset.

data.isna().sum()

Output :

age         0
sex         0
bmi         0
children    0
smoker      0
region      0
charges     0
dtype: int64
dataset does not contain null values.

Step 4:
Plotting the classes that are present in the 'region' Column with respect to their frequency.

sns.catplot(x='region', kind="count", data= data);

Step 5:

Plotting the classes that are present in the 'sex' Column with respect to their frequency.

sns.catplot(x='sex', kind="count", data= data);

Step 6:

Plotting the classes that are present in the 'smoker' Column with respect to their frequency.

sns.catplot(x='smoker', kind="count", data= data);

Step 7:

Creating the numerical plot for the continuous variables that are present in the dataset.

def num_plot(x,c='g'):
    plt.figure(figsize=(16,8))
    sns.distplot(data[x],color=c)
    plt.show()
    print(10*'----',x,10*'----')
    print('MIN: ',data[x].min())
    print('MIN: ',data[x].max())
    print('MIN: ',data[x].mean())
    
    

Step 8:

Maximum users age  are between 18 and 22

num_plot('age')

The minimum age is 18.

The maximum age is 64

The mean age is 39.20702541106

Step 9 :

num_plot('bmi')

This variable shows the normal distribution.

The minimum BMI is 15.96.

The maximum BMI is 53.13.

The mean BMI is 30.66339686.

Step 10 :

num_plot('children')

Most of the users are unmarried.

The minimum no of children is 0.

The maximum no of children is 5.

The mean no of children is 1.0949177.

Step 11:

num_plot('charges')

Most of the charges are below 100000

The minimum charge is 1121.8739.

The maximum charge is 63770.42801.

The mean charge is 13270.422265.

plt.figure(figsize=(16,8))
sns.scatterplot(y=np.arange(len(data)),x=data['age'],hue=data['charges'],palette='viridis',size_order='big')
plt.show()

Step 13 :

AGE VS BMI

plt.figure(figsize=(16,8))
sns.scatterplot(y=data['bmi'],x=data['age'],hue=data['charges'],palette='viridis',size_order='big')
plt.show()

Step 14 :

Plotting the BMI who has greater than paying the 50000.

plt.figure(figsize=(16,8))
data[data['charges']>50000]['bmi'].plot(kind='hist',color='m')
plt.show()

Step 15 :

Plotting the age who has greater than paying the 50000.

plt.figure(figsize=(16,8))
data[data['charges']>50000]['age'].plot(kind='hist',color='m')
plt.show()

STEP 16:

Checking if there is a relationship any between the mean insurance of smokers and non smokers.

mean_smo, mean_non_smo = data['charges'][data['smoker'] == 'yes'].mean(), data['charges'][data['smoker'] == 'no'].mean()
mean_smo, mean_non_smo

Output :

mean_male, mean_female = data['charges'][data['sex'] == 'male'].mean(), data['charges'][data['sex'] == 'female'].mean()
mean_male, mean_female

Output :

(13956.751177721893, 12569.578843835347)

We can conclude from above that the mean insurance charge for males and females is almost the same.

Step 18 :

Check for BMI higher than the normal range - Assumed normal range as 18.5 to 25.

mean_bmi_large, mean_bmi_normal = data['charges'][data['bmi'] > 25].mean(), data['charges'][data['bmi'] <= 25].mean()
mean_bmi_large, mean_bmi_normal

Output :

(13946.476035324473, 10284.290025182185)

We can conclude from above that the mean insurance charge for people having higher than normal BMI is more as the are more likely to suffer from diseases

Step 19 :

Check for BMI higher than the normal range - Assumed normal range as 18.5 to 25.

mean_young, mean_old = data['charges'][data['age'] < 35].mean(), data['charges'][data['age'] >= 35].mean()
mean_young, mean_old

Output ;

sns.heatmap(data.corr(), annot = True, vmin=-1, vmax=1, center= 0)

data = data.drop(['children','region'], axis = 1)
data.head()

cat_var = ['sex','smoker']
data['sex'] = pd.get_dummies(data['sex'], sparse=True)
data['smoker'] = pd.get_dummies(data['smoker'], sparse=True)

def coladd (age,smoker,bmi):
    if age>35 and smoker ==1 and bmi >25:
        return 1
    else:
        return 0

data['High_Risk'] = data[['age','smoker','bmi']].apply(lambda x: coladd(*x), axis=1)

data['High_Risk'].value_counts()

Output :

0    819
1    519
Name: High_Risk, dtype: int64.

mean_high_risk, mean_not_high_risk = data['charges'][data['High_Risk'] == 1].mean(), data['charges'][data['High_Risk'] == 0].mean()
mean_high_risk, mean_not_high_risk

Output :

def coladd1 (smoker,bmi):
    if smoker ==1 and bmi >25:
        return 1
    else:
        return 0

data['Medium_Risk'] = data[['smoker','bmi']].apply(lambda x: coladd1(*x), axis=1)

Checking the same analysis for medium risk individuals

mean_med_risk, mean_not_med_risk = data['charges'][data['Medium_Risk'] == 1].mean(), data['charges'][data['Medium_Risk'] == 0].mean()
mean_med_risk, mean_not_med_risk

Output :

X = data[['age', 'sex', 'bmi', 'smoker','High_Risk', 'Medium_Risk']]
Y = data['charges']

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state = 100)

Model Building

Linear regression :

Training the Model by using the training dataset.

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

Predicting the results.

y_pred_linear = regressor.predict(X_test)

Calculating the mean squared error and variance

print("Mean squared error: %.2f"
      % mean_squared_error(y_test, y_pred_linear))
print('Variance score: %.2f' % r2_score(y_test, y_pred_linear))

Output :

Mean squared error: 30637726.75
Variance score: 0.79.

Visualizing the results using scatter plot.

plt.scatter( y_pred_linear, y_test,  color=['red'])

Model Building using the lasso regression.

Train the lasso regression model using the training set.

from sklearn.linear_model import LassoCV
lasso_eps = 0.0001
lasso_nalpha=20
lasso_iter=10000
model_lasso= LassoCV(eps=lasso_eps,n_alphas=lasso_nalpha,max_iter=lasso_iter, normalize=True,cv=5)
model_lasso.fit(X_train,y_train)

print(list(zip(model_lasso.coef_,X_train.columns)))
print(model_lasso.intercept_)

[(263.9842580546879, 'age'), (-0.0, 'sex'), (425.4205810355094, 'bmi'), (-20037.03930661138, 'smoker'), (-0.0, 'High_Risk'), (-4617.541287287641, 'Medium_Risk')]
8903.607745718311.
Predicting the test set results.

y_predited_lasso = model_lasso.predict(X_test)

plt.scatter(y_predited_lasso,y_test)
plt.xlabel("Predicted Insurance")
plt.ylabel("Actual Insurance")