Machine Learning Approach for House Price Prediction

Chowhaan, M. Jagan and Nitish, D. and Akash, G. and Sreevidya, Nelli and Shaik, Subhani (2023) Machine Learning Approach for House Price Prediction. Asian Journal of Research in Computer Science, 16 (2). pp. 54-61. ISSN 2581-8260

[thumbnail of Sreevidya1622023AJRCOS101262.pdf] Text
Sreevidya1622023AJRCOS101262.pdf - Published Version

Download (574kB)

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.

Item Type: Article
Subjects: STM Open Press > Computer Science
Depositing User: Unnamed user with email support@stmopenpress.com
Date Deposited: 16 Jun 2023 06:24
Last Modified: 03 Oct 2024 03:56
URI: http://journal.submissionpages.com/id/eprint/1577

Actions (login required)

View Item
View Item