TrackFit

Product Introduction

1. TrackFit is a Streamlit-based web application designed to predict calories burned during exercise using machine learning models trained on user-specific parameters. It integrates BMI calculation, workout duration analysis, and physiological metrics to generate personalized fitness insights.
2. The core value of TrackFit lies in its ability to automate calorie prediction through optimized model selection, enabling users to make data-driven decisions for fitness planning and progress tracking.

Main Features

1. TrackFit automatically selects between SVM, Logistic Regression, or Random Forest models based on user inputs such as BMI, age, and workout duration to ensure optimal prediction accuracy.
2. The application generates a unique User ID for each session and stores historical predictions in a CSV file, allowing users to review trends and export data for external analysis.
3. TrackFit provides visualizations of historical workout data and feature importance plots for Random Forest models, offering transparency into how specific variables influence calorie-burn predictions.

Problems Solved

1. TrackFit addresses the challenge of accurately estimating calorie expenditure during exercise without relying on generic formulas or wearable devices, which often lack personalization.
2. The product targets fitness enthusiasts, gym trainers, and health-conscious individuals seeking a customizable tool to monitor workout efficiency and long-term progress.
3. Typical use cases include tracking calorie burn across multiple workouts, comparing the effectiveness of different exercise routines, and generating reports for fitness coaching or personal accountability.

Unique Advantages

1. Unlike fitness apps that use single-algorithm approaches, TrackFit dynamically switches between SVM, Logistic Regression, and Random Forest models based on user-specific parameters like BMI and age.
2. The integration of automated model selection with joblib-serialized pre-trained models ensures low-latency predictions while maintaining scalability for diverse user profiles.
3. TrackFit’s competitive edge stems from its open-source architecture, MIT-licensed codebase, and compatibility with GitHub-hosted CI/CD pipelines, enabling seamless customization for developers and enterprises.

Frequently Asked Questions (FAQ)

1. How does TrackFit choose between SVM, Logistic Regression, and Random Forest models? TrackFit evaluates user inputs such as BMI, age, and workout duration through a predefined decision logic to select the model with the highest historical accuracy for similar parameter combinations.
2. Where are user prediction histories stored, and how is data privacy handled? Predictions are saved locally in the data/history.csv file within the application’s directory, with no cloud storage or third-party data sharing, ensuring full user control over personal information.
3. Can I retrain the machine learning models with custom datasets? Yes, the train_models.py script allows retraining using modified datasets, provided the data follows the schema of the original training data included in the data/ directory.
4. What dependencies are required to deploy TrackFit in a production environment? The requirements.txt file specifies critical libraries including Streamlit 1.28.2, scikit-learn 1.2.2, pandas 2.0.3, and joblib 1.3.2, which must be installed via pip for full functionality.
5. Does TrackFit support real-time data input from wearable devices? Current versions rely on manual input via the Streamlit UI, but the open-source codebase can be extended to integrate APIs from devices like Fitbit or Apple Watch using additional Python modules.