Smart Property Pricing: A Machine Learning Approach to Real Estate Valuation

This study focuses on predicting real estate prices using machine learning techniques to improve theaccuracy and efficiency of property valuation. The research leverages diverse datasets, including propertyfeatures, location attributes, market trends, and economic indicators, to train machine learning models forreliable price predictions. Popular algorithms such as linear regression, decision trees, random forests, andgradient boosting methods are employed and compared for their predictive performance. Advancedtechniques like hyperparameter tuning, feature engineering, and cross-validation are utilized to enhancemodel accuracy. The study also emphasizes the interpretability of models to ensure that predictions alignwith market realities and stakeholder expectations. By providing data-driven insights, the research aims tosupport stakeholders, including buyers, sellers, and real estate professionals, in making informed decisionsin a competitive housing market.