Health-Insurance-Cost-Prediction-Web-App

Medical Health Insurance Cost Prediction |TechStack Used: Python, Pandas, Matplotlib, ML Models, GUI, Tkinter, Streamlit | • Performed EDA on Kaggle’s Medical cost personal dataset having the features like age, sex, bmi, children, smoker, region & charges. • Trained the model using Linear Regression, SVR, Random Forest Regression & Gradient Boosting Regression out of which Gradient Boosting Regression predicts with maximum r2 score of 0.87 • Saved the model using Joblib, developed the GUI using Tkinter, build and deployed the web application using Streamlit that predicts the insurance cost by entering the features value in web application.