A machine learning web app that predicts annual medical insurance charges from a patient's age, BMI, smoking status, and other demographics using a scikit-learn Random Forest pipeline.

Medical Insurance Cost Forecast Model utilizes machine learning techniques to predict insurance costs based on individual characteristics such as age, sex, BMI, number of children, smoking status, and region.

These projects used machine learning for healthcare tasks, including breast cancer classification, diabetes prediction, and medical insurance cost estimation, showcasing the versatility of models like decision trees, random forests, SVM, and linear regression.

Medical Insurance Cost Prediction

Developed a Streamlit-based web app featuring regression prediction projects for House Price, Car Price, Gold Price, Medical Insurance Cost, Big Mart Sales, and Calories Burnt using various machine learning models.

A comprehensive Medical Insurance Premium Prediction system featuring a Streamlit web app and FastAPI backend. Uses machine learning algorithms (Random Forest, XGBoost, Gradient Boosting) to predict insurance premiums based on demographics and health factors. Includes 1,340-record dataset, trained models, and production deployment on Railway.

Displays the amount of insurance premium charges for your current age, gender, bmi, having kids or not, smoker or non-smoker and region.