House Price Prediction System Using Bagging Based Ensemble Learning

One of the busiest and most vital sectors of any economy is the real estate sector. It is crucial for investors, buyers, sellers, and policymakers to have a precise estimate of home values. This article explains how the creation of numerous advanced machine learning models can be utilized to predict home values. Prediction is made using the most popular models, the most favored datasets, the most instructive features, and the proper evaluation procedures. Size, location, market conditions, and economic conditions are just a few of the physical and nonphysical aspects that make it difficult to accurately predict home prices. Real estate professionals, banks, and government agencies can now analyze trends and create complex models to forecast housing prices more accurately than they could with traditional analytical models thanks to digital advancements, particularly the use of machine learning in the housing sector. Using bagging-based ensemble learning, five machine learning models are hybridized to predict accuracy. In terms of correlation, R-squared, RMSE, and accuracy, our suggested ensemble model is outperforming the current model.