DESIGN AN AGILE OF MACHINE LEARNING TO PREDICTIVE HOUSE PRICING AND TARGETING SEGMENTED MARKET

Abstract

Because of too high expectations or having a wrongly segmented target market, the developer hasn't received a good response from the target market. Developers need a new marketing tool that is based on data. The use of machine learning systems as marketing tools to help solve the problems in house price prediction is an important topic in the real estate industry. The design of machine learning will use CRISP-DM as a framework and to analyze using linear regression and random forest as the best possible accuracy. Besides that, to find a potential market, we will use K-Means as a clustering method. The modeling and experiments to design a machine learning engine that can predict a range of selling prices using linear regression can give maximum accuracy and analyze the target market. The research focusing on different attributes will bring different dominant attributes to the table too.