Nadaehab24/Egypt-Real-Estate-Data-Analysis-Machine-Learning-Insights

A project to clean, analyze, visualize, and explore predictive modeling for real estate data in Egypt using Python and basic machine learning techniques. The goal is to understand property market trends, identify factors affecting prices, and experiment with simple ML models to see how property features may influence pricing. # Egypt Real Estate Data Analysis & Machine Learning Insights A project to **clean**, **analyze**, **visualize**, and **explore predictive modeling** for real estate data in Egypt using Python and basic machine learning techniques. The goal is to understand property market trends, identify factors affecting prices, and experiment with simple ML models to see how property features may influence pricing. --- ## Introduction This project focuses on analyzing a **Real Estate Listings dataset** collected from various regions in **Egypt**. The dataset includes important property details such as **price, location, area, number of bedrooms, bathrooms, property type, and furnishing status**. The main objectives are: - To clean and preprocess the raw dataset. - To explore and visualize patterns and relationships in property listings. - To experiment with a **basic machine learning model** for understanding how property features affect pricing. - To build a simple **dashboard** for visual exploration and summary. The project is organized into five main stages: 1. **Data Cleaning & Preprocessing** 2. **Exploratory Data Analysis (EDA)** 3. **Feature Engineering & Modeling** 4. **Dashboard & Visualization** 5. **Final Reporting** --- ## Dataset Description The dataset contains **10,326 rows (records)** and **17 columns (features)**, representing property listings from various Egyptian cities and neighborhoods. Each record includes information such as: - **title** – Listing headline. - **location** – City or area of the property. - **price** – Property price (may contain mixed formats like “1,200,000 EGP”). - **rooms** – Number of rooms. - **bathrooms** – Number of bathrooms. - **area** – Total property area in square meters. - **latitude / longitude** – Geographic coordinates. - **compound / developer / finishing** – Construction or furnishing details. - **description** – Short text describing the property. ### Detected Issues Before Cleaning - Missing values in seve …