| --- |
| title: Calorie Expenditure Predictor |
| emoji: 🔥 |
| colorFrom: purple |
| colorTo: red |
| sdk: streamlit |
| sdk_version: 1.31.0 |
| python_version: '3.10' |
| app_file: app.py |
| pinned: false |
| license: mit |
| --- |
| |
| # Calorie Expenditure Prediction - Kaggle Playground Series S5E5 |
|
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| This repository contains a machine learning application deployed to Hugging Face Spaces that predicts calories burned during physical activity. This project was developed as part of a "Become a Pro" Data Science challenge. |
|
|
| ## Project Overview |
| The goal of this project is to provide high-precision caloric expenditure estimates based on physiological and exercise-related metrics. The model was trained on a dataset of 750,000 records, focusing on memory efficiency and non-linear relationship modeling. |
|
|
| ## Technical Details |
| - **Model:** XGBRegressor (Extreme Gradient Boosting) |
| - **Performance:** |
| - **R-Squared:** 0.9962 |
| - **RMSE:** 3.80 |
| - **Environment:** Python 3.10 |
| - **Key Techniques:** |
| - Manual Categorical Mapping (Female: 1, Male: 0) to optimize memory. |
| - Histogram-based tree method (`tree_method='hist'`) for handling large-scale tabular data within 8GB RAM constraints. |
|
|
| ## Features Used |
| - `Sex` (Encoded) |
| - `Age` |
| - `Height` |
| - `Weight` |
| - `Duration` |
| - `Heart_Rate` |
| - `Body_Temp` |
|
|
| ## Local Setup |
| To run this project locally on VS Code with Python 3.10: |
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| 1. Create a virtual environment: |
| ```bash |
| python -m venv venv |
| source venv/bin/activate # On Windows use: venv\Scripts\activate |
