Calories Burnt Prediction using Machine Learning

Predicting calories burnt using machine learning involves estimating calorie expenditure based on factors like age, gender, heart rate, and exercise duration. This project uses regression models (e.g., Linear Regression, Random Forest, Gradient Boosting) trained on datasets containing fitness metrics. Key steps include data preprocessing (handling missing values, encoding categorical variables, standardization), feature selection (e.g., heart rate and duration), and model evaluation using metrics like Mean Squared Error (MSE) and R-squared. Hyperparameter tuning enhances model performance, making it suitable for fitness apps, wearable devices, and health programs.

Challenges include ensuring accurate input data and accounting for interpersonal variability in metabolism and fitness levels. Despite these challenges, this project is a practical application of data science in fitness tracking, offering insights for personalized health and wellness. 

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