This project predicts an individual's Stress Stage (Low, Moderate, High) using Machine Learning techniques based on features like mental health scores, lifestyle habits, and demographic information. The project leverages data preprocessing, exploratory data analysis, model training, and evaluation to build a robust stress classification system.

Literature-benchmarked proof of concept for biomarker-guided seed-stage climate resilience under combined heat and drought stress using intervention ranking and machine learning prediction.

This project uses machine learning to identify infertility risks in men and women at an early stage by analyzing medical data. By combining multiple models, it provides accurate predictions that support doctors in timely diagnosis, better treatment decisions, and reduced stress for patients.