Data-Driven Real Estate Validation: Advanced Predictive Modeling Techniques

Accurate prediction of house price is an important problem in the real estate market, with significant implications to buyers, sellers and investors. This research investigates creating a reliable regression-based model using state of the art machine learning algorithms in order to improve the accuracy of estimating residential property values. The method proposed consists of a detailed data preprocessing, feature engineering, and model stacking to overcome issues related to missing data, high dimensionality, and the necessity of performing a model-appropriate feature transformation. For better performance and predictability, a model end with an ensemble for the purpose of prediction and explaining the factors that can cause housing prices is presented in way that would help ass in decision making. Neighborhood, size, and condition of the property is among the most important factors that contribute to determining the price. Language: English Project Background Due to the complexity of real-estate markets, and the also varied nature of related influencing features this project aims at linking raw housing data and accurate price estimation by employing data-driven, predictive regression models. The results of this study may help inform decision making by stakeholders in the real estate universe.