Calories Burnt Prediction

04. Calories Burnt Prediction

04. Calories Burnt Prediction

A machine learning project that predicts the number of calories burnt during exercise based on physical and activity parameters. The system uses an XGBRegressor model trained on features like age, height, weight, workout duration, heart rate, body temperature, and gender to provide accurate calorie estimates via an interactive Streamlit web application.

Key Highlights:

- Machine Learning Model: XGBRegressor for robust and accurate predictions.

- Interactive Web App: User-friendly Streamlit interface with dark mode UI.

- Data Analysis: Jupyter notebook for exploratory data analysis and model training.

- Pre-trained Model: Includes saved model and scaler for immediate predictions.

- Comprehensive Features: Considers multiple user metrics for precise calorie estimation.

Technologies Used: Python, XGBoost, scikit-learn, Pandas, NumPy, Streamlit, Pickle, Jupyter Notebook.

Usage: Enter personal and exercise details in the web app and click "Predict Calories Burnt" to receive real-time calorie burn estimates.