Prediction and Analysis of House Price Through Machine Learning Approach

Abstract

In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.