The primary objective of this project is to develop an accurate, efficient, and data-driven model that can predict medical insurance premiums based on various personal, health, and lifestyle factors. The project follows a structured approach to developing an accurate and efficient machine learning model for predicting medical insurance premiums. It involves data collection, preprocessing, model selection, evaluation, and deployment to ensure reliability and practical applicability.
By leveraging historical data and advanced algorithms, ML models can identify key factors influencing insurance costs such as age, BMI, smoking status, medical history, and geographic location.
Age - The age of the customer
Sex - Gender of the customer (male or female)
BMI - Body Mass Index, measure of body fat based on height and weight
Children - No. of children/dependents covered under the insurance policy
Smoker - Smoking status of the customer
Region - Geographic region of the customer
Charges - Medical insurance premium charged to the customer
The following are some conclusions that can be drawn from the graphs as mentioned in Figure :
a. The charges are higher for Males as compared to Females, but the difference is not much. Hence, we can say approximate equal charges for both the genders.
b. The charges are nearly uniform across the four specified Regions.
c. The premium imposed on Smokers is thrice than that for non-smokers.
