40% OFF Calories Burnt Prediction Web App – AI Project for Health Enthusiasts
Calories Burnt Prediction is a Python-based machine learning web application built with Streamlit. It uses a Random Forest Regressor to estimate calories burned during exercise from gender, age, height, weight, workout duration, heart rate, and body temperature.
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- PythonMachine LearningBootstrapHTML/CSS
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- Source codeDatabase
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- Instant download after payment
Project Overview
Calories Burnt Prediction is a machine learning web application developed using Python and Streamlit. It estimates the number of calories burned during exercise based on personal and workout details entered by the user. The application processes the supplied information through a trained Random Forest Regressor and displays the predicted calorie value.
Available Features
- Calorie Prediction: Estimate calories burned during exercise using the supplied workout information.
- Interactive Web Interface: Enter personal and workout details through Streamlit widgets.
- User Input Fields: Accept gender, age, height, weight, workout duration, heart rate, and body temperature.
- Machine Learning Integration: Use a trained Random Forest Regressor to generate predictions.
- Sidebar Navigation: Navigate between the application's pages.
- Prediction Page: Enter details and view the predicted calorie value.
- Code Page: Access the project's source code through the application.
- About Page: View information about the project's purpose.
Which Technologies Are Used in This Project?
- Python: Implements the application logic.
- Streamlit: Creates the interactive web interface.
- scikit-learn: Supports the Random Forest Regressor model.
- pandas: Supports data handling.
- joblib: Loads the trained machine learning model.
More Details and Full Documentation
Visit the official UPDATEGADH project page for more details, screenshots, and installation instructions.
Calories Burnt Prediction Web App – AI Project for Health Enthusiasts
- Prediction Page
- Personal and Workout Input
- Calorie Prediction Using Random Forest Regressor
- Prediction Result Display
- Code Page
- About Page
- Sidebar Navigation
- Trained Model Loading Using joblib
- Data Handling Using pandas
Step 1: Install Python 3.10 or later and Visual Studio Code.
Step 2: Extract the project files and open the project folder in VS Code.
Step 3: Open Terminal > New Terminal.
Step 4: Create a virtual environment
python -m venv venv
Step 5: Activate the environment on Windows
venv\Scripts\activate
Step 6: Install the dependencies.
If requirements.txt is available
pip install -r requirements.txt
If it is not available
pip install streamlit scikit-learn pandas joblib
Step 7: Locate the main Python file containing the Streamlit application code.
Step 8: Run the application. Replace app.py with the actual filename if necessary
streamlit run app.py
Step 9: Open the application in your browser
http://localhost:8501
Important: The project must include its trained model file and any required data files. Check the supplied source code for their exact filenames and locations.
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