A web application that predicts whether a patient has malaria and identifies the specific strain (P. falciparum, P. malariae, or P. vivax) based on symptoms, vitals, and medical history, before lab tests are conducted. It uses three independently trained machine learning models and displays results with confidence scores.

This project builds an end-to-end machine learning pipeline to predict customer churn using the Telco dataset. It applies real-world data preprocessing, feature engineering, and multiple ML models with recall optimization for business impact. The final system is production-ready with model serialization using Joblib for deployment.