Affiliation:
1. Vel Tech High-tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai, Tamil Nadu, India.
Abstract
Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations of different ML
techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.
Reference18 articles.
1. Anand G. Rawool, Dattatray V. Rogye, Sainath G. Rane, Dr. Vinayk A. [2021]. "House Price Prediction Using Machine Learning." Iconic Research and Engineering Journals, Volume 4, Issue 11, ISSN: 2456-8880.
2. Ruchi Vyas and Jitendra Sharma. "An Algorithm to Predict Real Estate Price using Machine Learning." Asian Journal of Computer Science and Technology, Vol.12, No.1, 2023, pp. 31-34.
3. Chowhaan, M. J., Nitish, D., Akash, G., Sreevidya, N., & Shaik, S. (2023). Machine Learning Approach for House Price Prediction using machine learning. Asian Journal of Research in Computer Science, 16(2), 54-61. Article no. AJRCOS.101262.
4. N. N. Ghosalkar and S. N. Dhage, "Real Estate Value Prediction Using Linear Regression," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-5, doi: 10.1109/ICCUBEA.2018.8697639.
5. Truong, Q., Nguyen, M., Dang, H., and Mei, B. (2020). Housing Price Prediction via Improved Machine Learning Techniques. Procedia Computer Science, 174, 433-442. DOI: 10.1016/j.procs.2020.06.111