Calories Burnt Prediction
A simple project based on Calories Burnt Prediction which is developed as a web application to help people and fitness enthusiasts calculate how many calories they burn during exercise. This project focuses on applying machine learning to real-life fitness scenarios, making the process of tracking workouts smarter and more accessible.
The system is built using modern web technologies and is designed to provide an easy-to-use interface where users can enter personal and workout details like gender, age, height, weight, workout time, heart rate, and body temperature. Once the information is provided, the app calculates the estimated calories burnt during exercise. More than just a technical project, this app introduces students to practical applications of machine learning while also promoting healthy lifestyle habits. It is an excellent project idea for students, fitness lovers, or anyone who wants to learn how machine learning works in real-life applications.
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Project Details
| Project Name | Calories Burnt Prediction |
|---|---|
| Language/s Used | Python |
| Python Version | 3.x (recommended: 3.10+) |
| Framework/Library Used | Streamlit, scikit-learn, pandas, joblib |
| Type | Web Application |
| Developer | UPDATEGADH |
Available Features
This professionally built Calories Burnt Prediction web application includes the following features:
Real-time Calorie Prediction – The app can instantly predict the number of calories burned based on the user’s input, providing quick and accurate results for every exercise session.
Streamlit-based Web Interface – Built using Streamlit, the application provides a simple and interactive interface, making it easy for anyone to use without prior technical knowledge.
User Input Fields – Users can enter personal and workout-related details such as:
Gender
Age
Height
Weight
Duration of workout
Heart rate
Body temperature
These inputs help the machine learning model generate more accurate calorie predictions.
ML Model Integration (Random Forest Regressor) – The app uses a Random Forest Regressor to analyze the data and predict calories burnt efficiently, combining accuracy with speed.
Clean UI with Sidebar Navigation – The interface is designed to be user-friendly with sidebar navigation, allowing users to switch between pages effortlessly.
‘Prediction’, ‘Code’, and ‘About’ Pages – The app includes dedicated pages for:
Prediction – Where users can input their data and get calorie predictions
Code – Provides access to the project’s source code for learning purposes
About – Gives an overview of the project and its purpose
This combination of features makes the Calories Burnt Prediction web app a complete, interactive, and educational tool for students, fitness enthusiasts, and anyone interested in understanding real-life applications of machine learning.
How It Works
Once the user inputs their personal metrics and exercise stats, the app processes the data through a trained machine learning model and displays the predicted calories burnt instantly.
- The model is loaded via
joblib - Input form built with Streamlit widgets
- Backend logic processes data and predicts calories burnt using the Random Forest Regressor
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