International Journal For Multidisciplinary Research

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Real Estate Price Predcition System Using Hybrid Lasso Algorithm

Author(s) Moganarangan, Kishorre Raj, Ashathulla, Devasanjay, Shalinthraj Adhithyan
Country India
Abstract House Price prediction system is a website where the user/ buyer can be able to predict the accurate cost of real estate. To analyse the pertinent characteristics and the best models for predicting the price of homes, a literature review is done.. The model will then use the user's data, and the user will be able to view the predicted price of the property they are selling or looking to buy in a matter of seconds. The feature selection process is done with a type of regression called LASSO regression. A kind of linear regression that makes advantage of shrinkage is called LASSO regression. So we are proposing a system that uses Hybrid LASSO Regression algorithm to do the Feature Selection. Hybrid LASSO Regression is hybrid of both LASSO and Ridge Regression. When models exhibit significant levels of multi-collinearity or when you wish to automate specific steps in the model selection process, such as variable selection and parameter elimination, this specific sort of regression is ideally suited. This would greatly help academics and housing developers identify the most important factors that influence home values and recognize the most effective machine learning model to follow when conducting the field investigation.
Keywords Machine Leraning, Regression, Lasso, Hybrid Lasso, Ridge
Field Engineering
Published In Volume 5, Issue 2, March-April 2023
Published On 2023-03-15
Cite This Real Estate Price Predcition System Using Hybrid Lasso Algorithm - Moganarangan, Kishorre Raj, Ashathulla, Devasanjay, Shalinthraj Adhithyan - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.1764
DOI https://doi.org/10.36948/ijfmr.2023.v05i02.1764
Short DOI https://doi.org/gr2krj
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