Predicting the Housing Price Direction using Machine Learning Techniques

The phenomenon of the falling or rising of the house prices has attracted interest from the researcher as well as many other interested parties. There have been many previous research works that used various regression techniques to address the question of the changes house price. This work considers the issue of changing house price as a classification problem and applies machine learning techniques to predict whether house prices will rise or fall. This work applies various feature selection techniques such as variance influence factor, Information value, principle component analysis and data transformation techniques such as outlier and missing value treatment as well as box-cox transformation techniques. The performance of the machine learning techniques is measured by the four parameters of accuracy, precision, specificity and sensitivity. The work considers two discrete values 0 and 1 as respective classes. If the value of the class is 0 then we consider that the price of the house has decreased and if the value of the class is 1 then we consider that the price of the house has increased.