House Price Prediction System Using Ensemble Learning Approach

The real estate industry is among the most active and critically important industries for any economy. An accurate forecast of the prices of houses is very important to the buyers, sellers, investors and the policy makers. In years gone by, other methods of valuation have been used, but new advancements have been made in machine learning meaning the prediction speeds and the level of accuracy have been boosted. In this article, I would like to know how the development of various sophisticated models of ML can be used to forecast house prices: the most used models, the most favorite datasets and informative features, as well as the appropriate evaluation measures. Real estate is a continually expanding business that is prominently involved in the world economy. To precisely estimate house prices is always a daunting task due to various factors that vary from physical to non-physical factors include size, location, market, and economic conditions. Digital advancements especially, the incorporation of machine learning in the analysis of financial markets, housing and other sectors has enabled real estate specialists, banks and other institutions and government bodies analyze trends and develop sophisticated models to predict housing prices more effectively than conventional analytical models. In the context of this article’s expanse, we provide a deeper look into the components of house price prediction, with additional information on datasets, feature engineering, specific algorithms, optimization tactics, known difficulties, and future developments.