ML Diabetes Predictor

ML Diabetes Predictor is a machine learning web application that lets users input 8 medical parameters — pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age — and instantly receive a diabetes risk prediction. The backend is built with Flask and uses a Random Forest Classifier (20 estimators) trained on a cleaned diabetes dataset. The ML pipeline handles missing-value imputation (mean for glucose/blood pressure, median for skin thickness/insulin/BMI) before training. The trained model is serialized with pickle for fast inference. The app includes a clean HTML form UI, a /predict endpoint for predictions, and an /about page showing model accuracy. Containerized with Docker and deployed on Render.