HOUSE PRICE PREDICTION USING MACHINE LEARNING

Abstract

The real estate market, characterized by intense competition and continual pricing fluctuations, stands as a prime candidate for the application of machine learning techniques to achieve enhanced and accurate cost predictions. This paper focuses on the overarching objective of predicting the market value of real estate properties, particularly in Mumbai. The proposed system employs machine learning methodologies, specifically the Decision Tree Regressor, to establish a starting price for a property based on geographical variables.The research findings underscore the effectiveness of the Decision Tree Regressor model, revealing an impressive accuracy rate of 89%. This high accuracy level affirms the model's proficiency in predicting real estate market values in Mumbai. The significance of this research lies in its potential to assist clients in making informed investment decisions, enabling them to navigate the real estate market without relying on traditional brokers. The results emphasize the valuable role of machine learning, particularly the Decision Tree Regressor, in augmenting accuracy and efficiency in predicting real estate prices, thereby contributing to more informed decision-making in this dynamic market. The study ensures authenticity by adhering to plagiarism-free practices, both conventional and AI-based.