π AI-Powered Egyptian Real Estate Price Predictor | XGBoost Model (RΒ² = 0.65) | Streamlit Web App
# π Egypt Real Estate Price Predictor
AI-Powered Property Valuation Using XGBoost Regression
Predict real estate prices across Egypt with 95% confidence intervals | Trained on 15,791 listings
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## π Table of Contents
- π Features
- π οΈ Tech Stack
- π Dataset
- π Quick Start
- π Project Structure
- π€ Model Performance
- π― How It Works
- π Calibration System
- π§ Data Pipeline & Cleaning
- π€ Contributing
- π Acknowledgments
- π License
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## π Features
### **For Users (Web App)**
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**Smart Price Prediction** - AI-powered estimates using XGBoost regression
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**113+ Locations** - Coverage across Cairo, Alexandria, North Coast, Red Sea & more
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**18 Property Types** - Apartments, Villas, Chalets, Penthouses, iVillas, etc.
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**Payment Method Adjustment** - Cash vs Installments pricing (+15% premium for installments)
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**95% Confidence Intervals** - Statistical uncertainty ranges (Β±4.9M EGP)
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**Smart Calibration** - Corrects model bias for realistic market prices
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**Responsive Design** - Beautiful dark theme UI optimized for mobile & desktop
### **For Developers (Code & Research)**
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**Complete ML Pipeline** - Data cleaning β Feature engineering β Training β Evaluation β Deployment
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**4 Trained Models Compared** - Linear Regression, KNN, Random Forest, XGBoost
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**Production Ready** - Streamlit Cloud deployment ready with requirements.txt
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**Well Documented** - Jupyter notebook with step-by-step explanations
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**Reproducible** - Full dataset + cleaning code included for retraining
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**Open Source** - MIT License, free to use and modify
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## π οΈ Tech Stack
### **Machine Learning**
| Technology | Version | Purpose |
|------------|---------|---------|
| | 2.0+ | Primary regression model (best performer) |
| | 1.9.0 | Preprocessing, feature engineering, model evaluation |
| | 2.0+ | Data manipulation & cleaning |
| | 1.24+ | Numerical operations |
| | 1.10+ | Sparse matrix handling |
### **Web Application**
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