This project aims to develop a predictive model estimating insurance coverage costs for customers based on their attributes and product choices. The dataset includes transaction and quote details for policy purchasers. The objective is to predict quoted coverage costs, considering customer traits and 7 customizable product options.

A Data Science Machine Learning approach to predict the best candidates to be targeted for a marketing campaign

In this project we will predict the cost required for a patient depending on his/her health conditions.

this is ml project where we perform linear regression ,here we train the model and then predict charges acc. In this we use multicollinear linear regression.

🚀 This repository contains the code for predicting the success of Falcon 9's first-stage landing. The project uses historical data 📊 to forecast landing outcomes 🌍, providing insights into the cost-effectiveness 💰 of SpaceX's reusable rockets 🔁.

Live ML app that predicts which GP practices will overspend on NHS prescribing. Upload a raw NHSBSA file, get at-risk practices ranked by predicted overspend. Random Forest (R²=0.95) built with scikit-learn and Streamlit.

Healthcare machine learning research portfolio focused on predictive modeling, Medicare claims analytics, and healthcare cost prediction using CMS data.

End-to-end healthcare ML project modeling high-cost insurance members, combining cost driver analysis, feature engineering, and risk classification.

Production-style regression project for predicting insurance claim amounts using advanced modeling techniques, feature analysis, and business-driven insights.

Predicting medical insurance costs using machine learning in Python.

I compare the accuracy of health cost prediction of four regression models: Linear, Lasso, Ridge, and Elastic Net Regression.

Web app using univariate regression to predict the cost of opening a new franchise store.

End-to-end Streamlit ML pipeline on healthcare claims, predicts cost, fraud detection, dashboards via containerized deployment.

End to end FEMA disaster recorvery cost prediction using XGBoost, FastAPI, Streamlit, and feature engineering on FEMA public dataset

Regression-driven FinOps module for ShieldCraft AI. Predicts future compute spend with RMSE monitoring (≤4.7% target) and automates budget enforcement using Step Functions to optimize infrastructure cost reduction.

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.