Abstract: This study presents the application of machine learning techniques to the prediction of real estate/house prices on two real datasets that were obtained from Kaggle, one from Melbourne developed by Anthony Pino and the other from Boston created by D. Harrison and D.L. Rubinfeld. There is a dearth of literature regarding machine learning research on housing price prediction in India. This work attempts to develop this prediction engine for user use in the real world by reviewing the use of current machine learning methods on two radically dissimilar datasets. The results show that altering the algorithms can have a significant impact on accuracy. Furthermore, a subpar dataset may have a detrimental impact on the predictions. It also offers enough evidence to determine which algorithm is most appropriate for this task.

Keywords: Machine Learning, Real Estate, House Price, Price Prediction, Algorithm.

| DOI: 10.17148/IARJSET.2024.11751