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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM13705

Regression Modelling For Real Estate Pricing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM13705

Volume:

6 |

5 |

May 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SK

Sayama Kazi

SS

Snehal Sarangi

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Abstract

The real estate market is a standout the most attentive about pricing and keeps changing. The housing market is a champion among the most engaged with respect to valuing and continues to change. It is one of the superb fields to apply the thoughts of AI on instructions to improve and anticipate the expenses with high precision. There are three factors that impact the cost of a house which incorporates states of being, ideas and area. The current structure incorporates assessing the cost of houses with no assumptions for market costs and cost increase. The goal of the paper is to construct a predictive model for evaluating the cost based on the aspects that affect the price of houses.

KEYWORDS

Linear regression Random Forest Regressor XGBoost Regressor SVM Regressor.

Cite This Article

Sayama Kazi, Snehal Sarangi, "Regression Modelling For Real Estate Pricing," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 5, May 2022.

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Publication Date

5/27/2022

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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