Enhancing Real Estate Market Insights through Machine Learning: Predicting Property Prices with Advanced Data Analytics

Abstract

This paper presents a comprehensive study on real estate cost prediction in Mumbai using machine learning regression techniques. The dataset consists of properties in metropolitan cities, gathered by scraping information from various web sources. Initially, 16 features were considered, and through feature selection techniques, six influential features were identified for the predictive models. Diverse machine learning regression strategies were implemented, and the efficacy of the models was assessed utilizing appropriate metrics. The results demonstrate the accuracy of the selected features and predictive models in estimating real estate costs. This investigation adds to the real estate domain by leveraging machine learning and web- scraped data, furnishing valuable perceptions for stakeholders within the realm of real estate. Said perceptions abet in the formulation of well-informed determinations pertaining to property investments, valuation methodologies, and market tendencies. Additionally, the feature selection process sheds light on the key factors significantly impacting real estate costs in metropolitan cities like Mumbai.