Obesity Risk Prediction Project

Project Summary

This project uses a dataset from Kaggle Playground Series S4E2 to predict the risk of obesity. The goal is to classify individuals into different categories of obesity risk based on their age, gender, physical activity, and eating habits.

Project Structure

• src/obesity_risk.py
• notebook/Obesity_Risk.ipynb

Target Variable: NObeyesdad

• Insufficient Weight: 0
• Normal Weight: 1
• Obesity Type I: 2
• Obesity Type II: 3
• Obesity Type III: 4
• Overweight Level I: 5
• Overweight Level II: 6

Dataset

The dataset includes both numerical and categorical features:

• Numerical: Age, Height, Weight, BMI
• Categorical: Gender, Transportation Method, Physical Activity

Project Steps

1. Data Preprocessing and Analysis: The dataset is loaded, cleaned, and checked for missing values. Target labels are transformed into numbers.
2. Feature Engineering: New features are created based on the existing data. For example, BMI is calculated and added to the dataset.
3. Modeling and Evaluation:
   • The data is split into training and testing sets. Various classifiers (KNN, CART, Random Forest, GBM, LightGBM, CatBoost) are used to make predictions.
   • Models are evaluated using metrics like accuracy, precision, recall, and F1-score.
4. Hyperparameter Tuning: The best parameters for each model are found using RandomizedSearchCV.

Classifiers Used

• KNN (K-Nearest Neighbors)
• SVC (Support Vector Classifier)
• CART (Decision Tree)
• RF (Random Forest)
• GBM (Gradient Boosting Machine)
• XGBoost
• LightGBM
• CatBoost

Model Performance

The models are evaluated using different metrics. After tuning the parameters, the best model was LightGBM. It performed the best compared to other models.

Metrics Used:

• Accuracy
• Precision
• Recall
• F1 Score

How to Use

1. Clone the repository:

   git clone https://github.com/aysecnkci/ObesityRisk-ML-Modeling.git
   
   

2. Navigate to the src directory and run the obesity_risk.py file:

   cd src
   python obesity_risk.py

3. Alternatively, you can explore the project step-by-step in the Jupyter Notebook:

   jupyter notebook notebook/Obesity_Risk.ipynb

4. The project requires the following Python libraries, which you can install using:

   pip install -r requirements.txt

Results

After tuning the hyperparameters, the best model was CatBoost, which achieved high accuracy. Key results from the analysis include:

• People with low physical activity are at higher risk of obesity.
• Gender and transportation method are important features for predicting obesity risk.

Future Work

• Add More Data: Including genetic and mental health data could improve the model's accuracy and predictive power.
• Deep Learning Models: Neural networks and other deep learning techniques could enhance the model’s performance further.

Contributing

If you want to contribute to this project, follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/AmazingFeature

3. Commit your changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the branch:

   git push origin feature/AmazingFeature

5. Open a pull request.

License

This project is licensed under the Apache License. See the LICENSE file for more details.

Resources

• Kaggle Playground Series S4E2
• LightGBM Documentation
• CatBoost Documentation
• Scikit-learn Documentation

Contact

If you have any questions about this project, feel free to contact me:

• GitHub Profile
• LinkedIn Profile