House Resale Price Prediction Using Classification Algorithms

Now a days house resale is majorly seen in metro cities. The market demand for housing is always increasing every year due to increase in population and migrating to other cities for their financial purpose. Prediction of house resale price for long-term temporary basis is important especially for the people who stays who will stay the long time period but not permanent and the people who do not want to take any risk during the house construction. In this paper, the resale price prediction of the house is done using different classification algorithms like Logistic regression, Decision tree, Naive Bayes and Random forest is used and we use AdaBoost algorithm for boosting up the weak learners to strong learners. Several factors that are affecting the house resale price includes the physical attributes, location as well as several economic factors persuading at that time. Here we consider accuracy as the performance metrics for different datasets and these algorithms are applied and compared to discover the most appropriate method that can be used the reference for determining the resale price by the sellers.