Taqdeer: Resolving the Real-Estate Negotiation Phase Using Machine Learning to Determine Selling Price

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Taqdeer is a machine learning-powered platform designed to enhance the transparency and efficiency of real estate negotiations in Saudi Arabia. The project addresses challenges such as unreliable data, inconsistent pricing, and poor access to accurate property valuations. By using historical transaction records from the Ministry of Justice and integrating real-time economic and geographic data, the platform estimates property prices with high accuracy. The system is built with a user-friendly web interface that features interactive maps, property filtering options, and visual data displays. At its core, the project uses a machine learning model (CatBoost) to predict property prices based on features like location, type, size, and market trends. GIS (Geographic Information System) integration helps visualize price data across regions. Taqdeer aims to support both buyers and sellers by offering data-driven, fair market price estimates, empowering users to make informed decisions and avoid overpaying. This tool has the potential to improve real estate transparency and contribute to the long-term stability of the housing market in Saudi Arabia. Tools & Technologies Used: - Machine Learning: CatBoost, Scikit-learn - Data Handling: Pandas, NumPy - Backend: Flask - Frontend: HTML, CSS, JavaScript - Database: MySQL - Deployment: Docker - GIS: Google Maps API

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