House Price Analysis with Multi Model Prediction

The phenomenon of the falling or rising of the house prices has attracted interest from the research worker and as several alternative interested parties. There ar several previous researches that used numerous regression techniques to deal with the question of the changes house value. This work considers the {problem} of adjusting house value as a classification problem and applies machine learning techniques to predict whether or not house costs can rise or fall. This work applies numerous feature choice techniques like variance influence issue, data price, principle element analysis and information transformation techniques like outlier and missing price treatment in addition 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 2 distinct values zero and one as several categories. If the worth of the category is zero then we tend to take into account that the value of the house has cut and if the worth of the category is one then we tend to take into account that the value of the house has accrued.