Calories Burnt Prediction is a supervised learning dataset for predicting exercise energy expenditure. It aims to use an individual's physiological characteristics and exercise status information to predict the number of calories burned during a workout.

This dataset uses physiological and exercise state characteristics as its main inputs, with a clear task objective and a well-defined data structure, making it suitable for individualized exercise energy consumption prediction research. In terms of data composition, the dataset uses a single exercise record as the basic sample unit. Each sample contains multidimensional individual attributes and exercise process indicators, corresponding to a continuous numerical calorie consumption value. Its field structure is as follows:

• User_Id: Unique identifier for the user
• Gender: gender
• Age: Age
• Height: height
• Weight: weight
• Duration: Duration of exercise
• Heart_rate: Heart rate during exercise
• Body_temp: Body temperature during exercise
• Calories: The number of calories burned during exercise.