Python, Spyder, Data integrity, Prediction, Data Modeling.
Abstract
House price forecasting is an important topic of real estate. The literature attempts to drive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transaction to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs. Moreover, experiments demonstrate that the combination of stepwise and support vector machine that is based on mean squared error measurement is a competitive approach. The goal of the study is through analyzing a real historical transactional dataset to derive valuable insight into the housing market. It seeks useful models to predict the value of a house given a set of its characteristics. Effective model could allow home buyers or real estate agents to make better decisions.
