ABSTRACT
In addressing the formidable challenge of predicting housing prices, this project introduces an advanced machine learning-based model designed to significantly enhance accuracy. The complexity of diverse location data poses a significant hurdle, necessitating a comprehensive approach. Our suggested methodology creates a framework that aims for more accurate predictions by combining advanced machine learning algorithms with sophisticated data pre-processing techniques. Our model's performance is demonstrated through rigorous examination utilizing real-time housing price data, demonstrating significant gains over current techniques. By offering a reliable and effective method, this research makes a substantial contribution to the field of housing price prediction. The potential impact of our model extends to facilitating better decision-making in real estate transactions, where accurate pricing predictions play a pivotal role. By addressing the intricate variability in data associated with diverse location points, our model stands as a noteworthy advancement in the quest for more reliable and insightful tools for real estate professionals and stakeholder.
