Laksh Nath
Laksh Nath

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Models Home » Generic Models » Predictive Modelling » Medical Insurance Cost Prediction using Machine Learning with Python

Medical Insurance Cost Prediction using Machine Learning with Python

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Model Overview


Insurance company especially health insurance company need to analyze how a customer is disease-prone. We will try to find the risk based on the customer's data.

If the Health_Risk can be estimated accurately then insurance cost can be calculated using this formula.

Health Insurance Cost = Base Cost + α * Health Risk

We try to investigate how accurately we can estimate the health risk of a person.
We can begin the usecase by applying the Linear regression model to the Medical Insurance dataset to foresee future Insurance costs for the people.

Linear Regression Algorithm :
Linear Regression is the first machine learning algorithm based on ‘Supervised Learning’. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x).
When there is a single input variable (x), the method is referred to as ‘Simple Linear Regression’. When there are multiple input variables, the method is referred to as ‘Multiple Linear Regression’.
Simple regression equation : Y = A + B * X
X → Input variable (training data)
B → Coefficient of X
A → Intercept (Constant)
Y → Predicted Value ( Calculated from A, B and X)
Let’s understand this model more clearly by doing the below exercise where we apply Linear Regression to our dataset.

Columns Description :
- Age: Age of primary beneficiary
- Sex: Primary beneficiary’s gender
- BMI: Body mass index (providing an understanding of the body, weights that are relatively high or low relative to height)
- Children: Number of children covered by health insurance / Number of dependents
- Smoker: Smoking (yes, no)
- Region: Beneficiary’s residential area in the US (northeast, southeast, southwest, northwest)
- Charges: Individual medical costs billed by health insurance

Lets now start the usecase by importing the required dependencies

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn import metrics

import csv

So the next step is data collection and analysis for that we need to load the data

#loading the data 

medicalinsurance = pd.read_csv('/content/insurance.csv')

Now let us check the first five rows of the dataset by using the head function.


#checking first 5 rows of data

medicalinsurance.head()


let us find the number of rows and columns in the dataset

#finding number of rows and columns

medicalinsurance.shape


the next step is to get some information about the dataset.


#getting info about the dataset

medicalinsurance.info()


In this dataset we have 3 categorical features which are sex, smoker and region.
Now let us check for any missing values present in the dataset.


#checking missing values

medicalinsurance.isnull().sum()


Let's now analyze the data by using some plots by getting the statistical measures of the dataset.


#statistical measures of data

medicalinsurance.describe()


Now we are going to find the distribution of age value


#distribution of age

sns.set()

plt.figure(figsize=(6,6))

sns.distplot(medicalinsurance['age'])

plt.title('Age Distribution')

plt.show()

 



Now let us do it for the gender column.


#distribution of gender

plt.figure(figsize=(6,6))

sns.countplot(x='sex', data=medicalinsurance)

plt.title('Sex Distribution')

plt.show()


medicalinsurance['sex'].value_counts()


#distribution of bmi column

plt.figure(figsize=(6,6))

sns.distplot(medicalinsurance['bmi'])

plt.title('BMI Distribution')

plt.show()


The normal BMI range for a person lies between 18.5 and 24.9 while looking from the above figure most of people are overweight which can affect insurance costs.
Now let us do the distribution for the children column.


#distribution of children column

plt.figure(figsize=(6,6))

sns.countplot(x='children', data=medicalinsurance)

plt.title('Children')

plt.show()

 




medicalinsurance['children'].value_counts()


Now let us do it for the smoker column.


#distribution of smoker column

plt.figure(figsize=(6,6))

sns.countplot(x='smoker', data=medicalinsurance)

plt.title('smoker')

plt.show()


medicalinsurance['smoker'].value_counts()


#distribution of region column

plt.figure(figsize=(6,6))

sns.countplot(x='region', data=medicalinsurance)

plt.title('region')

plt.show()




medicalinsurance['region'].value_counts()


#distribution of charges 

plt.figure(figsize=(6,6))

sns.distplot(medicalinsurance['charges'])

plt.title('Charges Distribution')

plt.show()


Data Pre-Processing 
As we can see in the dataset we have 3 categorical columns which are in  text form so let us convert it into numerical values. We will encode the categorical values.


# encoding sex column

medicalinsurance.replace({'sex':{'male':0,'female':1}}, inplace=True)

# encoding 'smoker' column

medicalinsurance.replace({'smoker':{'yes':0,'no':1}}, inplace=True)

# encoding 'region' column

medicalinsurance.replace({'region':{'southeast':0,'southwest':1,'northeast':2,'northwest':3}}, inplace=True)

Let us now split the features and target 


X = medicalinsurance.drop(columns='charges', axis=1)

Y = medicalinsurance['charges']

print(X)



print(Y)


Splitting the data into Training data & Testing Data


X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)

print(X.shape, X_train.shape, X_test.shape)


Let us now train the model


# loading the Linear Regression model

regressor = LinearRegression()

regressor.fit(X_train, Y_train)


Now let us evaluate the model


# prediction on training data

trainingprediction =regressor.predict(X_train)

# R squared value

rtrain = metrics.r2_score(Y_train, trainingprediction)

print('R squared vale : ', rtrain)



# prediction on test data

testprediction =regressor.predict(X_test)

# R squared value

rtest = metrics.r2_score(Y_test, testprediction)

print('R squared vale : ', rtest)



Building a Predictive System


inputdata = [31,1,25.74,0,1,0]

#changing inputdata to numpy array

inputarray = np.asarray(inputdata)

#reshaping the array

inputreshaped = inputarray.reshape(1,-1)

prediction = regressor.predict(inputreshaped)

print(prediction)

print('The insurance cost is USD',prediction[0])


Now Lets save this model by importing pickle library.


import pickle

pickle.dump(regressor,open('/content/model.pkl','wb'))



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