FitTrack is a machine learning powered health analytics web application that predicts exercise calorie burn and daily calorie requirements using physiological data such as age, weight, height, heart rate, exercise duration, and body temperature.
The application is built using Python and Streamlit and utilizes an ensemble machine learning model combining XGBoost, LightGBM, and CatBoost to generate accurate calorie burn predictions.
For model transparency and interpretability, the application integrates SHAP (SHapley Additive Explanations), allowing users to visualize and understand which physiological factors most influence calorie burn predictions.
Additionally, FitTrack includes a nutrition logging system based on an Indian food nutrition dataset, enabling users to track daily intake of calories, protein, carbohydrates, and fat.
Interactive dashboards display daily calorie progress, remaining calorie targets, and macro nutrient distribution, helping users effectively monitor their diet and exercise impact.
Overall, FitTrack provides a comprehensive and interactive platform for users to track physical activity, manage nutrition intake, and gain insights into the physiological factors influencing their calorie expenditure.
