This project offers an intuitive web application for nano-entrepreneurs to predict health insurance costs. Leveraging a machine learning model, it estimates expenses based on personal factors, aiding financial planning. Built with Flask and scikit-learn, it provides a user-friendly interface for quick and accurate insurance premium estimation.

A data-driven solution that helps Assur'Aimant, a French insurer, optimize insurance premium estimation. This project uses a predictive model to determine the cost of a potential client's premium, enabling efficient and accurate pricing for insurance companies

A GitHub repository hosting an insurance prediction model employing Decision Tree, Random Forest, and KNN algorithms, with KNN achieving the highest accuracy score of 87.7% and tested for response on unseen data.

This is a web app where a user can signup to the website first and then login to access the website. Then, he/she can give their age, select his/her gender, bmi, number of children, select whether he/she is a smoker or not, and select his/her region. Gradient Boosting Regressor is used in this project which gives the best accuracy of 89.798.

Machine Learning project to predict health insurance premiums using Random Forest. Achieves 66% R² score with 13.23% MAPE on 1M records. Built with Python, scikit-learn, and pandas.

Predicting health insurance costs using custom Linear Regression (Gradient Descent) vs Scikit-learn. Includes EDA and data preprocessing.

Predict annual medical insurance costs instantly using Machine Learning! This AI system analyzes age, BMI, smoking habits, and demographics to estimate healthcare expenses with 90% accuracy. Built with Linear Regression, featuring 8+ visualizations and comprehensive data analysis.

Full-stack Machine Learning Web App for predicting yearly medical insurance charges.

Medical Insurance Price Prediction using Machine Learning and Python to estimate healthcare insurance costs based on demographic and health factors.

Predicting medical insurance charges using regression models — EDA, feature engineering, XGBoost & Scikit-Learn

Prudential Life Insurance Assessment Prediction

Predicting medical insurance charges using regression models like Random Forest, KNN, and Linear Regression.

Machine Learning project that predicts customer insurance purchase behavior using demographic and financial data with Python and Scikit-learn.

End-to-End Health Insurance Premium Prediction using Random Forest and FastAPI. Dockerized and deployed on Render.

Life Insurance Claim Prediction using Machine Learning. This project predicts the probability of an insurance claim based on customer and transaction data. Deployed with Render and integrated with a frontend.

Predicting medical insurance costs using Linear Regression optimized from scratch with Genetic Algorithm (GA).

A FastAPI-based backend application that provides RESTful APIs for predicting insurance premiums using a trained machine learning model. Designed with a modular structure for easy integration, scalability, and future enhancements.

Machine Learning project to predict term deposit subscription using Random Forest, SMOTE and XGBoost.

This is a school project of mine. I decided to do a process of data analysis & visualizations, feature engineering, model creation/testing, model evaluation, and metrics visualizations.