mo7medSa3d/Real-Estate-Price-Prediction-Project-in-Egypt-Dubizzle-
Domain:
socioeconomicRecord type:
project
Creator:
mo7
Host:
This project focuses on building a high-accuracy Machine Learning Regression Model to predict residential unit prices within the Egyptian real estate market, utilizing data aggregated from an online property classifieds platform.
# 🏠 Real Estate Price Analysis & Prediction in Egypt (Dubizzle Properties)
## 🌟 Project Overview
This project aims to build a high-accuracy **Machine Learning model** to predict residential property prices in the Egyptian market, using data collected from real-estate listing platforms.
* **Main Objective:** Identify the key factors influencing property prices and build a reliable **Regression model**.
* **Languages & Tools:** Python (Pandas, NumPy, Matplotlib/Seaborn, Scikit-learn, XGBoost).
* **Current Model Status:** Achieved **moderate accuracy** (**R² ≈ 0.51** after feature engineering), indicating that the most critical improvements depend on **increasing dataset size** and **adding missing features**.
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## 💾 Dataset and Features
The dataset was collected from real-estate listings and contains **4,500 rows** (before cleaning).
| Feature (English) | Description | Example |
|-------------------|-------------|---------|
| `price` | Property price (the **target** variable) | 4,492,000 EGP |
| `area` | Property area (m²) | 151 m² |
| `beds` | Number of bedrooms | 3 |
| `baths` | Number of bathrooms | 3 |
| `compound` / `location` | Compound or neighborhood name | First Settlement / Diar1 |
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## 🧹 Data Preprocessing Methodology
Several advanced data cleaning and feature engineering steps were applied to improve model stability:
1. **Initial Cleaning:** Removed symbols, normalized values, and converted numeric columns using `Numeric` types.
2. **Outlier Removal (IQR):** Applied to all numerical features (`price`, `area`, `baths`, `beds`).
3. **Advanced Location Encoding (Target Encoding):**
* The categorical `compound` feature was replaced with a new numeric feature: **`Compound_Value`**.
* This represents the **average property price per compound**, making it the strongest predictor.
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## 🤖 Modeling and Performance
Three regression models were tested:
| Model | R² Score | Notes |
|-------|----------|-------|
| **Linear Regression** | 0.27 | Weak …