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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
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:
1. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate |