Medical Insurance Prediction Using Machine Learning

Medical Insurance Prediction using Machine Learning Model

Business Case :

1. What are the most important factors that affect medical expenses?

2. How well can machine learning models predict medical expenses?

3. How can machine learning models be used to improve the efficiency and profitability of health insurance companies?

Dataset : Kaggle
IDE used : Pycharm
Programming language : Python, sklearn, Pandas, Numpy,matplotlib, seaborn

Required Packages:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler,PolynomialFeatures,OneHotEncoder
from sklearn.linear_model import LinearRegression,Ridge
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.model_selection import cross_val_score,train_test_split

medical_cost = pd.read_csv("/Users/m/Library/CloudStorage/OneDrive-Microsoft365/Sample Datasets Kaggle/medical_insurance.csv")
medical_cost.head()
medical_cost.replace('?',np.nan, inplace=True)
medical_cost.info()

is_smoker = medical_cost['smoker'].value_counts().idxmax()
medical_cost["smoker"].replace(np.nan,is_smoker, inplace=True)

#Age is continuous variable, replace with mean age
mean_age = medical_cost["age"].astype(float).mean(axis=0)
medical_cost["age"].replace(np.nan,mean_age,inplace=True)
#Update Data type for Age:
medical_cost["age"] = medical_cost["age"].astype(int)
# medical_cost["smoker"] = medical_cost["smoker"].astype(int)
print(medical_cost.head(10))
medical_cost["charges"] = np.round(medical_cost["charges"],2)
print(medical_cost.head(5))

###Exploratory Analysis : Implementing the box plot w.r.t Smoker:
sns.boxplot(x= "smoker",y="charges",
            data = medical_cost)
plt.show()
###Fitting a linear regression model : (Model Development):
x = medical_cost["smoker"]
y = medical_cost["charges"]

#Create transformer for one hot encoding the "smoker" column:
preprocessor = ColumnTransformer(
    transformers=[('smoker', OneHotEncoder(), ['smoker'])],
    remainder='passthrough'
)

#create a new dataframe with one-hot encoded categorical variables
z= pd.get_dummies(medical_cost[["age","sex",
                                "bmi","children",
                                "smoker","region"]])

#initialize the linear regression model
lm =LinearRegression()
lm.fit(z,y)
print("Value of Linear Regression is : ",lm.score(z,y))


#create a training pipeline that uses StandardScaler(), PolynomialFeatures() and LinearRegression()
#to create a model that can predict the charges value using all the other attributes of the dataset.

# Y and Z use the same values as defined in previous cells
Input =[('scale',StandardScaler()),('polynomial',
                                   PolynomialFeatures(include_bias=False)),('model',
                                                                          LinearRegression())]
pipe = Pipeline(Input)
z = z.astype(float)
pipe.fit(z,y)
ypipe =pipe.predict(z)

#print  R^2 score
print("value of R2 Co Ordination is ",r2_score(y,ypipe))

Value of R2 Co Ordination is 0.8455638928017903