Predicting-Insurance-Charges

This project combines predictive analytics with feature engineering in the healthcare/insurance domain.

The task is to predict healthcare cost of customers for a Health Insurance Company.

The variable 'charges' is the target variable.

insurance.csv

Column	Data Type	Description
age	int	Age of the primary beneficiary.
sex	object	Gender of the insurance contractor (male or female).
bmi	float	Body mass index, a key indicator of body fat based on height and weight.
children	int	Number of dependents covered by the insurance plan.
smoker	object	Indicates whether the beneficiary smokes (yes or no).
region	object	The beneficiary's residential area in the US, divided into four regions.
charges	float	Individual medical costs billed by health insurance.

The dataset requires some data cleaning and feature engineering to get a top predictive model.

I develop a regression model using XGBoost to predict insurance charges for customers of different demographic.

The process involves exploratory data analysis, data cleaning techniques and feature engineering.

Feature Engineering Steps Include:

1. Created a category bin for age called age_group ['Child', 'Young Adult', 'Middle-Aged', 'Senior']
2. Created a category bin for bmi called bmi_category ['Underweight', 'Normal', 'Overweight', 'Obese']
3. Created a variable to capture the risk associated with smoking

The XGBoost model was trained using a 5-fold cross validation.

The evaluation metric used is R-Squared.

I calculated the models feature importance and identified that the engineered features contributed signifficantly to the model's performance.