Calorie Burn Prediction
This project uses machine learning—especially the XGBoost algorithm—to predict how many calories a person burns during exercise. It takes inputs like age, gender, exercise type, and duration, and gives real-time predictions through a user-friendly web app.
Client
Personal Project
Service Provided
Machine Learning Understanding
The Goal:
The goal is to build a personalized, accurate, and interactive system that predicts calorie burn based on user-specific and exercise-related data. It aims to replace traditional, generalized methods with a smarter, data-driven approach that adapts to individual differences and provides real-time feedback.
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The Challenge:
Traditional calorie tracking methods like MET formulas or manual logs are often inaccurate and non-personalized. They don’t consider individual factors like age, weight, or exercise intensity. Most fitness apps also lack real-time prediction and interactive features. The challenge is to create a system that is accurate, user-friendly, and adaptable to different users and activities.
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The Result
The results of the Calorie Burn Prediction system demonstrate the effectiveness of machine learning in estimating energy expenditure during physical activities. Here's a breakdown of the key outcomes: 🔹 1. Prediction Accuracy The model was trained using the XGBoost algorithm, which outperformed other models like Random Forest, SVM, and Linear Regression. Evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) showed high accuracy in predicting calorie burn. The model was tested on unseen data (20% of the dataset), confirming its ability to generalize well across different user profiles and activities. 🔹 2. Real-Time Feedback The system provides instant calorie burn estimates based on user inputs like age, gender, weight, height, exercise type, and duration. It dynamically adjusts predictions based on changes in activity intensity or user details, making it suitable for live workout tracking. 🔹 3. Personalization By incorporating user-specific data, the system tailors predictions to individual fitness levels and body metrics. This personalization improves the relevance and usefulness of the feedback compared to generic fitness apps. 🔹 4. Visualization Features The web app includes interactive graphs that show: Calories burned over time Comparison between predicted and actual values Feature importance (e.g., which factors most influence calorie burn) These visual tools help users understand their progress and make informed decisions about their fitness routines. 🔹 5. Model Comparison The project tested multiple models: XGBoost: Best performance in terms of accuracy and speed. Random Forest: Good accuracy, robust to overfitting. SVM: High precision but slower on large datasets. Linear Regression: Simple and interpretable, but less accurate for complex patterns. Visual comparisons (e.g., scatter plots and line graphs) were used to show how each model performed against actual calorie data. 🔹 6. User Experience The system was built using Streamlit, offering a clean and responsive interface. Users can easily input their data and receive predictions without needing technical knowledge. The app also stores prediction history, allowing users to track their fitness journey over time.
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