Build, train and test a machine learning model to predict insurance cost based on customer features such as age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location.

Machine Learning models to predict Medical Insurance Premium Charges based on a person's age, sex, body mass index, smoking habits, number of children, and geolocation. Implemented Linear Regression using SCIKIT. Implemented an Artificial Neural Network. Link for certificate: https://coursera.org/share/04b53072c055c056ef0b3412cddf7bf7

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we will build, train and test a machine learning model to predict insurance cost based on customer features such as age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location.

A machine learning project that predicts medical insurance premiums based on customer data such as age, gender, BMI, number of children, smoking habits, and region.

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The purpose of this project is to develop machine learning models that can forecast Medical Insurance Premium Charges by considering factors such as age, gender, BMI, smoking status, number of children, and geolocation. SCIKIT was used to implement linear regression and put an artificial neural network into action.

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predict the health insurance cost incurred by indviduals bsed on their age,gender,BMI,number of children , smoking habit and geo_location

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Medical-Insurance-Premium-Prediction-with-Machine-Learning

Machine Learning with TensorFlow: Medical Insurance Premium Prediction

Machine Learning with Scikit-Learn: Medical Insurance Premium Prediction

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Developed a Machine Learning–based Medical Insurance Premium Prediction system using Random Forest regression with feature engineering (BMI) to estimate premium costs based on patient health attributes.

Medical Insurance Cost Prediction uses machine learning to estimate premiums based on factors like age, BMI, smoking, and region, helping insurers price policies accurately and providing customers with premium insights.

Premium Price Prediction App — A machine learning web application built with Streamlit that predicts a patient's medical insurance premium based on health-related inputs using a Random Forest Regressor model.

The Medical Insurance Price Prediction web application built with Streamlit. It predicts the estimated annual insurance premium for individuals based on their demographic and lifestyle information using a trained machine learning model.

This project is a health insurance premium prediction application built using machine learning and deployed with Streamlit. The model estimates insurance costs based on user inputs like age, income, medical history, and lifestyle.

A machine learning model that predicts medical insurance premiums based on factors like age, gender, BMI, smoking habits, and location. It uses regression algorithms to estimate costs and provides users with personalized insurance predictions.

The Medical Insurance Premium Predictor is a Streamlit-based machine learning web app that estimates insurance premiums using user details such as age, gender, BMI, smoking habits, children, and region.Built with Python, scikit-learn, and pandas, it provides accurate predictions , helping users plan their medical expenses better.

A machine learning project that predicts medical insurance premiums using demographic and health features. Includes data preprocessing, EDA, feature engineering, and multiple regression models, with XGBoost delivering the best prediction performance.

End-to-End Machine Learning project for predicting medical insurance costs using Polynomial Regression. Covers data preprocessing, feature engineering, model training, evaluation, and Streamlit deployment with an interactive UI for real-time premium prediction.

This project builds an end-to-end Health Insurance Cost Prediction system for an insurance company to estimate premiums based on user demographics, lifestyle, and medical history. The system uses machine learning models with segmentation logic and is deployed via a Streamlit web application for real-time predictions.

A machine learning–based web application that predicts an individual’s medical insurance coverage and premium using age, sex, income, and family size. Built in Python using PyCharm Community Edition with a Streamlit-based interactive user interface for real-time predictions.

Medical Insurance Cost Predictor is a machine learning web app that estimates annual insurance premiums using features like age, BMI, smoking status, children, gender, and region. It uses Linear Regression, Random Forest, and Gradient Boosting models and is deployed with Streamlit to provide instant predictions and visual insights.

A production-ready machine learning API that predicts insurance premiums based on user attributes such as age, BMI, smoking status, and medical history. The system uses a trained regression model and exposes prediction endpoints through FastAPI. It includes input validation with Pydantic, automatic API documentation via Swagger