This project focuses on predicting the calories burnt during physical activities based on key attributes like gender, age, height, weight, exercise duration, heart rate, and body temperature. By utilizing various machine learning algorithms, we aim to develop a model that accurately estimates the calories burnt.
The project uses the following machine learning algorithms for comparison and performance evaluation:
- XGBoost Regressor
- Random Forest Regression
- Decision Tree Regression
- Linear Regression
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XGBoost Regressor:
- A highly efficient and flexible gradient boosting algorithm that builds trees in a sequential manner, optimizing prediction by correcting errors from previous iterations.
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Random Forest Regression:
- An ensemble learning method that creates multiple decision trees and combines their predictions for more accurate and stable results.
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Decision Tree Regression:
- A non-parametric model that splits the dataset into smaller subsets based on conditions from input features, resulting in a tree-like structure for predictions.
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Linear Regression:
- A basic yet effective regression technique that models the relationship between the target and input features using a linear equation.
The dataset used in this project contains the following attributes:
- Gender: The gender of the individual (Male/Female)
- Age: Age of the individual (in years)
- Height: Height in centimeters
- Weight: Weight in kilograms
- Duration: Duration of the physical activity (in minutes)
- Heart_Rate: Heart rate during the activity (in beats per minute)
- Body_Temp: Body temperature during the activity (in degrees Celsius)
The target variable is the Calories Burnt, which will be predicted using the above features.