_____________________________________________________________________________________________________ *Corresponding author: E-mail: sreevidya1509@gmail.com; Asian J. Res. Com. Sci., vol. 16, no. 2, pp. 54-61, 2023 Asian Journal of Research in Computer Science Volume 16, Issue 2, Page 54-61, 2023; Article no.AJRCOS.101262 ISSN: 2581-8260 Machine Learning Approach for House Price Prediction M. Jagan Chowhaan a , D. Nitish a , G. Akash a , Nelli Sreevidya a* and Subhani Shaik a a Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJRCOS/2023/v16i2339 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://www.sdiarticle5.com/review-history/101262 Received: 03/04/2023 Accepted: 05/06/2023 Published: 15/06/2023 ABSTRACT In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. To analyze the key elements and the best predictive models for home prices, literature research is conducted. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Our results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers. Data Article