🔥 Calorie Expenditure Prediction System (Regression Model)

This is a machine learning–powered web application developed using Python, Flask, and a tuned XGBoostRegressor model to predict calorie expenditure during physical activity.

After training and tuning the regression model with XGBoostRegressor, the final optimized model is exported using Pickle and served through a backend built with Flask.

🚀 Features

• Predicts calorie expenditure (continuous numeric value) using health and activity indicators.
• Built using a tuned regression model (XGBoostRegressor).
• Interactive and clean web UI built using HTML/CSS powered frontend via Flask.
• Includes multiple engineered features to improve prediction accuracy.
• Uses a pre-trained model (best_xgbr_model_final.pkl).
• Lightweight, fast, and deployable on any Python-supported machine.

🏋️ Input Parameters

The model predicts calorie expenditure using the following inputs:

Feature	Type / Unit
Sex	Male / Female
Age	Numeric (years)
Height	cm
Weight	kg
Duration	minutes
Heart_Rate	bpm
Body_Temp	°C

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⚙ Engineered Features (used in training)

During model training, the following additional features were created and are required during prediction as well:

• Duration_Heart = Duration × Heart_Rate
• Duration_Temp = Duration × Body_Temp
• Age_Duration = Age × Duration
• Weight_Duration = Weight × Duration
• Height_Duration = Height × Duration
• HR_per_Weight = Heart_Rate / Weight

These features are automatically calculated during model training and are expected by the model during inference (prediction).

🧰 Technologies Used

• Python
• XGBoostRegressor – Core Machine Learning regression model
• Scikit‑learn – Preprocessing and model utilities
• Pandas / NumPy – Data preprocessing & feature engineering
• Flask – Web application backend
• Pickle – Model serialization
• Jupyter Notebook – Model training & evaluation
• HTML / CSS – Frontend design

📁 Files Included

datasets/train.csv datasets/test.csv

models/final_ml_model_6.ipynb models/best_xgbr_model_final.pkl

templates/index.html templates/result.html

static/styles.css

app.py README.md requirements.txt .gitignore

⚙️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/Kalana-S/Calorie-Expenditure-Predicting-System.git
   cd Calorie-Expenditure-Predicting-System

2. Create virtual environment (optional but recommended):

   python -m venv venv
   venv\Scripts\activate   # For Windows
   # OR
   source venv/bin/activate  # For macOS/Linux

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask app:

   python app.py

5. Open your browser and go to http://127.0.0.1:5000

📸 Screenshots

Homescreen

Prediction Result

🤝 Contribution

Contributions, issues, and feature requests are welcome! Feel free to open a pull request or start a discussion.

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.