ahmedmohsen01/New_Capital_Price_Prediction

A Streamlit web app for predicting apartment prices in Egypt using a Random Forest model. Users input property details like type, location, size, and amenities to get price predictions. Includes an analytical dashboard with average price, price per sqm, and distribution charts. ## 🏙️ Real Estate Price Prediction in Egypt 🇪🇬 This project is a **property price prediction tool** and **analytical dashboard** for residential listings in Egypt, with a focus on the **New Administrative Capital**. Built using **Streamlit**, **scikit-learn**, and **Pandas**, the application provides users with: ### 🔍 Features * **Machine Learning Model** (Random Forest) that predicts property prices based on: * Property type * Location * Apartment area (sqm) * Number of bedrooms and bathrooms * Selected amenities * **Interactive Streamlit App** with: * A clean UI and banner of Egypt’s New Capital * Dropdown inputs for property type and location to avoid invalid inputs * Real-time price prediction with visual feedback * Dashboard displaying: * Average apartment price * Average price per square meter * Minimum and maximum property prices * Price distribution histogram * **Outlier handling**: * Removes unrealistic entries (e.g., prices above 500M EGP or below 3M EGP) for better model accuracy ### 📦 Files Included * `app.py`: Streamlit application * `model.pkl`: Trained Random Forest regression model * `features.pkl`: Feature columns after one-hot encoding * `data.csv`: Cleaned and filtered dataset (used for analytics) ### 📈 Use Case This tool helps **buyers, real estate analysts, and developers** estimate property values and gain insights into market trends based on data scraped from online property listings. --- Let me know if you want to include example screenshots, deployment instructions (e.g., Streamlit Cloud), or badges (like “made with ❤️ in Egypt”). ## 📌 Reference This project is inspired by and built upon the work of Epsilon AI. We acknowledge their contribution to the open-source community.