Calorie Burn Tracker using Python & Machine Learning

The Calorie Burn Tracker is a web-based application designed to help users monitor, record, and analyze their daily calorie expenditure effectively. Physical fitness has become a necessity in modern lifestyles, yet many individuals struggle to maintain a consistent routine due to a lack of proper tracking tools. While wearable devices offer activity monitoring, they can be costly and sometimes lack customizability. The Calorie Burn Tracker project addresses this gap by providing an affordable, accessible, and customizable platform for tracking calories burned during physical activities.

Tech Stack Used

Frontend / Web Interface:

• Django (Python Web Framework) – Used to create the web interface for user input, displaying predictions, and managing data
• HTML5, CSS3, JavaScript – For rendering and styling web pages
• Bootstrap (optional) – For responsive UI components
• Django Templates – For dynamic web page rendering

Machine Learning / Backend Logic:

• scikit-learn – Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
• NumPy – For numerical operations and matrix manipulation
• Pandas – For handling and preprocessing datasets
• joblib – To save and load the trained machine learning model

Database:

• SQLite – Lightweight relational database used to store user data and predictions
• Django ORM (Object Relational Mapper) – Handles interaction between Django models and the SQLite database

Tools & Environment:

• Python 3.x – Core programming language used
• PyCharm – IDE for development
• Virtualenv / pip – For managing dependencies

Key Features of the Proposed System

a) User Registration and Profile Management
Users can register by entering personal details, which will be stored securely in the system’s database. This profile data will be used to personalise calorie calculations and generate custom recommendations.

b) Activity Logging
Users can select the type of activity from a predefined database, specify the duration, and indicate the intensity level. This ensures that calorie burn estimates are as accurate as possible given the available inputs.

c) Calorie Burn Calculation
The system uses the MET method to calculate calories burned based on:

• Activity type
• Duration of exercise
• User’s weight
  This ensures that the calculations are grounded in recognised scientific methods.

d) Progress Tracking and Reports
A historical log of all activities is maintained. Users can view progress reports with filters for daily, weekly, or monthly views. Graphs and charts are generated to make the data more visually engaging.

f) Responsive User Interface
The interface is designed for accessibility and ease of use, ensuring that even first-time users can navigate without confusion.

Calorie Burn Tracker: Output Screens

Home Page

User Registration

User Input Form

History

How to run the Calorie Burn Tracker Python ML Project

1. Download the zip file

2. Extract the file, copy calorie_project, folder and paste it on the desktop

3. Open PyCharm and Import the project in pycharm

4. Install three libraries (if not installed)

pip install joblib

pip install numpy

pip install scikit-learn

6. Run the Project using the following command

python manage.py runserver

Now, click the URL http://127.0.0.1:800,0 and the Project will run

Login Details

*************admin************

Username:  admin

Password: Test@123

*************User************

Username:  john12

Password: Test@123

Calorie Burn Tracker: Project Demo

Calorie Burn Tracker: Project, Report and PPT

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