🧠 Obesity Prediction System

A machine learning system to predict obesity levels using health and lifestyle data, with a Tkinter-based GUI.

📊 Dataset

• 2111 samples from Mexico, Peru, and Colombia
• 16 features (e.g., Age, BMI, Eating habits)
• Target: 7 obesity categories

⚙️ Preprocessing

• Created BMI feature
• Handled missing values (mean/mode)
• One-Hot & Label Encoding
• Outlier handling (IQR)
• Feature Scaling (StandardScaler)
• Selected top features (RandomForest)

🤖 Models

• Logistic Regression, SVM, KNN, Decision Tree
• Random Forest, Gradient Boosting, Neural Network
• Stacking (ensemble meta-model)

🖥️ GUI (Tkinter)

• Users enter data → get prediction

📁 Files gui.py: Interface

obesity_classification.py: Model training

.pkl files: Models & encoders

👨‍💻 Team 107 – Ain Shams University Supervised by T.A. Maryam El Sawaf Contributors: بسملة، مريم، فاطمة، نوران، عبدالرحمن، محمد