International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Prediction and Analysis of House Price Through Machine Learning Approach

Author(s) Manivel Kandasamy, Raju Shanmugam, Ayush Dave, Charvin Chawda, Krish Shah, Umang Seladiya
Country India
Abstract In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.
Keywords Lasso Regression Approach and Neural Network-Based Machine Learning Algorithm, Hosing Cost Prediction
Field Computer Applications
Published In Volume 5, Issue 4, July-August 2023
Published On 2023-08-24
Cite This Prediction and Analysis of House Price Through Machine Learning Approach - Manivel Kandasamy, Raju Shanmugam, Ayush Dave, Charvin Chawda, Krish Shah, Umang Seladiya - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.5255
DOI https://doi.org/10.36948/ijfmr.2023.v05i04.5255
Short DOI https://doi.org/gsm4zm

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