🔥 Calories Burn Prediction using Machine Learning

This project predicts the number of calories burned during exercise using machine learning techniques. It uses personal and exercise-related features to build an accurate regression model.

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📌 Project Overview

Calories burned during physical activities depend on several factors such as:

• Age
• Gender
• Height
• Weight
• Exercise duration
• Heart rate
• Body temperature

This project uses these features to train a machine learning regression model that predicts calorie expenditure effectively.

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🗂 Dataset Information

The project uses two datasets:

• calories.csv
  Contains calorie values burned by individuals.

• exercise.csv
  Contains exercise-related information such as duration, heart rate, and body temperature.

Both datasets are merged and processed to create the final training dataset.

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🔄 Project Workflow

Data Collection ↓ Data Preprocessing ↓ Feature Engineering ↓ Model Training ↓ Model Evaluation ↓ Prediction

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🧠 Machine Learning Approach

• Data preprocessing includes:

  • Handling categorical variables (e.g., Gender encoding)
  • Merging datasets
  • Feature selection

• Model used:

  • Regression-based machine learning model
    (e.g., XGBoost / Random Forest)

• Evaluation metrics:

  • Mean Absolute Error (MAE)

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost
• Jupyter Notebook

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📁 Project Structure

calories-prediction-ml/ │ ├── data/ │ ├── calories.csv │ └── exercise.csv │ ├── calories_burn_prediction.ipynb │ ├── Import Libraries │ ├── Load Dataset │ ├── Data Understanding (shape, info) │ ├── Data Cleaning & Preprocessing │ ├── Exploratory Data Analysis (Graphs) │ ├── Feature Engineering │ ├── Model Training │ ├── Model Evaluation │ └── Conclusion │ ├── README.md └── requirements.txt

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▶️ How to Run the Project

1. Clone the repository:

   git clone <repository-url>

Navigate to the project directory:

bash Copy code cd calories-prediction-ml Install required dependencies:

bash Copy code pip install -r requirements.txt Run the Jupyter Notebook:

bash Copy code jupyter notebook Then open calories_burn_prediction.ipynb.

📊 Results The model successfully learns the relationship between exercise parameters and calorie burn.

Visualization graphs help in understanding data distribution and model performance.

The trained model provides accurate calorie burn predictions.

🎯 Conclusion This project demonstrates a complete machine learning workflow, from raw data to prediction. It highlights good practices such as:

Data preprocessing

Clean project structure

Model evaluation

Proper documentation

👨‍💻 Author Ankush Rana Computer Science & Engineering (AI & ML)

⭐ Acknowledgements Dataset source: Kaggle

Libraries: Scikit-learn, XGBoost

Inspiration: Health & Fitness Analytics