https://github.com/arashabe/ml_obesity_estimation
This project aims to estimate obesity levels in individuals from Mexico, Peru, and Colombia based on their eating habits and physical condition. Using machine learning classification models, we analyze patterns in the dataset to predict obesity levels with high accuracy.
https://github.com/arashabe/ml_obesity_estimation
adaboost cross-validation machine-learning random-forest svm-model
Last synced: 13 days ago
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This project aims to estimate obesity levels in individuals from Mexico, Peru, and Colombia based on their eating habits and physical condition. Using machine learning classification models, we analyze patterns in the dataset to predict obesity levels with high accuracy.
- Host: GitHub
- URL: https://github.com/arashabe/ml_obesity_estimation
- Owner: arashabe
- Created: 2025-05-08T08:58:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-01T12:47:33.000Z (about 1 year ago)
- Last Synced: 2025-10-15T16:19:40.480Z (9 months ago)
- Topics: adaboost, cross-validation, machine-learning, random-forest, svm-model
- Language: Jupyter Notebook
- Homepage:
- Size: 2.99 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Obesity Level Estimation Using Machine Learning**
### [Statistical Learning](https://unibg.coursecatalogue.cineca.it/insegnamenti/2024/38091-MOD2/2021/8865/89?coorte=2024&adCodRadice=38091) - Master's in Computer Engineering (Data Science & Data Engineering Pathway)
## **Overview**
This project aims to estimate obesity levels in individuals from **Mexico, Peru, and Colombia** based on their **eating habits and physical condition**. Using **machine learning classification models**, patterns in the dataset are analyzed to predict obesity levels with high accuracy.
## **Dataset Information**
- **Source:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition)
- **Instances:** 2111
- **Features:** 16
- **Target Variable:** *NObesity* (Obesity Level)
- **Classes:**
- Insufficient Weight
- Normal Weight
- Overweight Level I
- Overweight Level II
- Obesity Type I
- Obesity Type II
- Obesity Type III
- **Data Generation:**
- **77% Synthetic** (via Weka & SMOTE)
- **23% Collected directly** from users via a web platform
## **Models Applied & Results**
Several **classification models** were implemented to evaluate predictive performance, leveraging **GridSearchCV for hyperparameter tuning** and **5-fold cross-validation** to ensure generalization.
### **Performance Summary**
| Model | Best Hyperparameters | CV Accuracy (%) | Test Accuracy (%) | Zero-One Loss | F1 Score (%) |
|----------------------|----------------------|----------------|----------------|--------------|-------------|
| **Logistic Regression** | ElasticNet (L1/L2) | **87%** | **87.36%** | **66.0** | **87.36%** |
| **Decision Tree** | Depth = 10, Entropy | **91%** | **93.68%** | **33.0** | **93.68%** |
| **Random Forest** | 400 Estimators, Entropy | **91%** | **91.95%** | **42.0** | **91.95%** |
| **SVM** | Linear Kernel, C=5 | **95%** | **96.74%** | **17.0** | **96.74%** |
| **AdaBoost** | Learning Rate = 0.6, 300 Estimators | **92%** | **88.12%** | **62.0** | **88.12%** |
### **Key Observations**
- **SVM achieved the highest test accuracy (96.74%)**, indicating superior generalization.
- **Decision Tree performed well (93.68%)**, but showed minor overfitting in training.
- **Random Forest provided stable predictions (91.95%)**, with lower variance compared to Decision Tree.
- **Logistic Regression**, serving as the baseline, performed moderately (87.36%).
- **AdaBoost**, despite strong cross-validation accuracy (92%), exhibited lower test accuracy (88.12%), suggesting generalization challenges.
## **Methodology**
1- **Data Preprocessing** → Standardization, PCA for dimensionality reduction, SMOTE for class balancing
2- **Model Training** → Decision Tree, Random Forest, AdaBoost, SVM, Logistic Regression
3- **Hyperparameter Optimization** → GridSearchCV for best parameter selection
4- **Cross-Validation** → 5-Fold CV to ensure robustness
5- **Model Evaluation** → Accuracy, F1 Score, Confusion Matrix, Precision, Recall
## **Installation & Dependencies**
To replicate the project, install the required dependencies:
```bash
pip install -r requirements.txt
```
**Dependencies:**
```
numpy
pandas
matplotlib
plotly
seaborn
scikit-learn
imbalanced-learn
xgboost
```
## Running the Project
1- Clone the repository
2- Install dependencies
```bash
pip install -r requirements.txt
```
3- Run the Jupyter Notebook
```bash
jupyter notebook
```
4- Execute the analysis
## Conclusion
This project demonstrates the effectiveness of **machine learning** in predicting obesity levels based on dietary and physical behavior data. The **ensemble models (AdaBoost & SVM) outperform traditional methods**, highlighting the impact of **boosting techniques** in classification problems.