---
title: BodyFatPredictor
emoji: 🧍‍♂️
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Streamlit app with BodyFat (%) predictor
license: mit
---

# 🧍‍♂️ Body Density Predictor – Ridge Regression

This project presents a **machine learning regression application** that predicts **human body density** from anthropometric measurements using a **Ridge Regression** model.  
The implementation follows the original **ML Olympiad – Predicting Wellness** competition setup, where **body density** is the primary target variable.

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## 📌 Project Overview

The main objectives of this project are:

- Predict **body density (Density)** from physical body measurements  
- Build a **stable and interpretable regression model** for a small dataset  
- Provide an **interactive Streamlit demo** for single and batch predictions  
- Clearly communicate model assumptions and limitations  

This project is intended for **educational and portfolio purposes**.

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## 📊 Dataset & Target

- **Input features:**  
  Anthropometric measurements such as age, height, weight, and body circumferences (neck, chest, abdomen, hip, thigh, etc.)

- **Target variable:**  
  `Density` (human body density)

The model is trained **directly on Density**, without predicting body fat percentage.

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## 🧠 Model Description

- **Model type:** Ridge Regression (L2-regularized linear regression)
- **Reason for model choice:**
  - Handles strong multicollinearity between body measurements
  - Performs well on small datasets
  - Produces stable and smooth predictions
  - Easy to interpret and deploy

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## 🧮 Feature Engineering

Only features that were used during training are applied in the app:

- **Waist-to-Hip Ratio:**  
Waist_hip = Abdomen / Hip

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- **Body Index (BMI, imperial units):**  
Body_Index = 703 × Weight / Height²

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Engineered features are calculated **only if required by the trained model**.

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## 📏 Units & Input Conventions

| Measurement | Unit |
|------------|------|
| Height | **Centimeters (cm)** (converted internally to inches) |
| Weight | **Pounds (lbs)** |
| Body circumferences | **Inches** |
| Model output | **Density** (unitless) |

⚠️ Correct units are essential for meaningful predictions.

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## 🖥️ Application Features

### 🔹 Single Prediction
- Interactive form with realistic input ranges
- Automatic unit conversion (cm → inches)
- Feature engineering and alignment
- Density prediction with plausibility warnings
- Display of final model inputs

### 🔹 Batch Prediction (CSV)
- Upload CSV files with required feature columns
- Automatic feature alignment
- Download predictions as a CSV file

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## ⚠️ Important Notes on Predictions

- Human body density typically lies within a **very narrow range (≈ 0.95 – 1.10)**  
- Predictions outside this range may indicate:
- Unrealistic input values
- Inputs outside the training distribution

The app provides **warnings** when predicted density values fall outside the typical physiological range.

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## 🧪 Limitations

- Small dataset size limits generalization
- Linear regression may oversimplify complex physiological relationships
- Predictions may be unreliable for extreme or unrealistic input values
- The model is not intended for medical or clinical use

These limitations are **explicitly communicated in the app interface**.

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## 🚫 Disclaimer

This application is **not a medical device**.  
All predictions are provided for **educational and demonstration purposes only** and must not be used for health diagnosis or treatment decisions.

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## 🧰 Tech Stack

- Python  
- pandas, NumPy  
- scikit-learn  
- Streamlit  
- joblib  

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## 📎 Repository Structure

├── app.py
├── ridge_model.pkl
├── requirements.txt
└── README.md

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## ✅ Conclusion

This project demonstrates a complete and transparent machine learning pipeline for predicting **body density** using Ridge Regression.  
By focusing directly on the competition target variable and avoiding unnecessary post-processing, the application provides **stable, interpretable, and scientifically consistent predictions**, making it well suited as a portfolio and learning project.

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