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.

Memprediksi nilai premi asuransi kesehatan (nilai biaya) dengan Machine Learning dan Deep Learning

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.

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.

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

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.

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

This project predicts medical insurance charges using machine learning models after performing data preprocessing, EDA, and feature engineering. It highlights key cost drivers like age and smoking status and uses trained models for accurate predictions. The entire workflow demonstrates an end-to-end approach to regression-based predictive modeling.

Prudential Life Insurance Assessment Prediction

predicting medical insurance costs based on individual characteristics, and for actuarial analysis.

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.

Machine learning project predicting insurance charges based on customer attributes using various regression models (Linear Regression, Random Forest, SVR, etc.) with feature analysis and model comparison.