Prediction of House Price Using Machine Learning

The trend of the sudden drop or constant rising of housing prices has attracted interest from theresearcher as well as many other interested people. There have been various research works that usedifferent methods and techniques to address the question of the changing of house prices. This workconsiders the issue of changing house price as a classification problem and discuss machine learningtechniques to predict whether house prices will rise or fall using available data. This work applies variousfeature selection techniques such as variance influence factor, Information value, principle componentanalysis, and data transformation techniques such as outlier and missing value treatment as well asdifferent transformation techniques. The performance of the machine learning techniques is measured bythe four parameters of accuracy, precision, specificity, and sensitivity. The work considers two discretevalues 0 and 1 as respective classes. If the value of the class is 0 then we consider that the price of thehouse has decreased and if the value of the class is 1 then we consider that the price of the house hasincreased.