This project uses an XGBoost Regression model to predict calorie expenditure from exercise. The model is trained on a dataset containing various features like gender, age, height, weight, and exercise intensity metrics to predict the number of calories burned. Dataset The project uses two datasets:
exercise_dataset.csv: Contains features related to the user and their exercise. calories.csv: Contains the target variable (calories burned).
Features
User_ID: Unique identifier for each user Gender: Male or female Age: Age of the user in years Height: Height of the user (likely in cm) Weight: Weight of the user (likely in kg) Additional exercise-related metrics (heart rate, exercise duration, etc.)
Dependencies
Python 3.x NumPy Pandas Matplotlib Seaborn scikit-learn XGBoost