The research project focused on the development of an algorithm aimed at predicting house prices. The primary objective was to create a robust and accurate model that could estimate the value of residential properties based on various features and data points. By analyzing historical housing data, incorporating factors such as location, property size, amenities, market trends, and economic indicators, the algorithm aimed to generate reliable price predictions. The project aimed to bridge the gap between subjective assessments and data-driven predictions, providing a standardized and objective approach to estimating house prices. Through machine learning techniques and statistical analysis, the algorithm would continuously learn and adapt, improving its predictive capabilities over time. The ultimate goal of the project was to assist buyers, sellers, and real estate professionals in making informed decisions based on more accurate price estimations, contributing to a more transparent and efficient housing market.

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