Statistical Analysis of Random Forest on Real Estate Prediction
Joylin Zeffora.A1, R. Shobarani2
1Joylin Zeffora. A, Research Scholar, Dr. M.G.R. Educational and Research Institute, Chennai, India.
2R. Shobarani, Professor, Dr. M.G.R. Educational and Research Institute, Chennai, India.
Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 19 June 2019 | PP: 640-644 | Volume-8 Issue-8S June 2019 | Retrieval Number: H11090688S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction models in real estate have a significant role to play in telling the future of the real estate industry. They have a role in forecasting that is essential to investors who use the information to come up with effective decisions. Random Forest model’s accuracy in estimating residential property prices are much better when compared to other models as the marginal error is comparatively less.
Keywords: Random Forest, Real Estate Model, Statistics, Predictive Analysis, Price Prediction.
Scope of the Article: Forest Genomics and Informatics