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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM22403

HOUSE PRICE PREDICTION AND RECOMMENDATION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM22403

Volume:

7 |

5 |

May 2023

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

MGAP

Mayur Gawade Aniket Patel

SB

Shantanu Barhanpurkar

OD

Onkar Deshmukh

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Abstract

Determining how much a house will sell for in a city is still a challenging and time-consuming task. This article’s goal is to make predictions about the coherence of non-housing prices. A crucial method to ease the challenging design is to use machine learning, which can intelligently optimize the best pipeline fit for a task or dataset. For individuals who will be residing in a home for an extended period of time but not permanently, it is essential to predict the selling price. Real estate forecasting is a crucial part of the industry. From historical real estate market data, the literature seeks to extract pertinent information. Land price bubbles grow as a result of real estate prices, which leads to macroeconomic instability. The government should look into the variables that drive up real estate prices so that it can use them as a guide to assist stabilize the area. There are many economic circumstances that are in play at the time also have an impact on the selling price of a home.

KEYWORDS

House Price Prediction and Recommendation Machine Learning Model Light Gradient Boosting Machine Random Forest Regression.

Cite This Article

Mayur Gawade Aniket Patel, Shantanu Barhanpurkar, Onkar Deshmukh, "HOUSE PRICE PREDICTION AND RECOMMENDATION," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023. https://doi.org/10.55041/IJSREM22403

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Publication Date

2/5/2023

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Double-Blind Peer Review

Review Rounds: 2

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

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