Predicting Insurance cost based on personal attributes like age, BMI, and lifestyle factors.
In this project, we develop a regression-based machine learning model to predict individual insurance costs.
The dataset includes key features such as age, gender, BMI, number of children, smoking status, and region. Using exploratory data analysis (EDA),
we uncover insights and visualize relationships between features and the target variable—insurance charges. Categorical variables are encoded properly, and outliers are handled to improve data quality.
We implement various regression algorithms including Linear Regression, Ridge Regression, and Random Forest Regressor to predict the insurance costs.
The model is evaluated using performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. Feature importance and residual analysis are also covered to ensure model reliability.
This project is perfect for beginners and intermediates looking to strengthen their regression modeling and real-world ML application skills.