Volume & Issue no: Volume 11, Issue 5, September - October 2022
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Title: |
Automated tool for the Prediction of House Price Using Machine Learning Techniques. |
Author Name: |
Dr. Dayanand G. Savakar, *Kirthi Galgali, Danesh Telsang |
Abstract: |
Abstract: This study uses three machine learning algorithms,
linear regression(LR),grid search Cv(GSCV),random forest
(RF) in the appraisal of property prices, It applies these methods
to examine a data sample of about 13,321 housing transactions
in a in Bangalore, and then compares the results of these
algorithms, In terms of predictive power, LR ,GSCV and RF
have achieved better performance , The three performance
metrics including associated prospective home buyer
considers multiple factors like as a location, size of the
land , power generation facilities many several features
selection and feature extraction algorithms combined with
Linear Regression, Most often,
with these two algorithms, The sale price of properties in cities
like Bengaluru depends on a number of interdependent factors ,
The size, location, and amenities of the property are important
variables that could determine the price. This article includes an
analytical investigation. has been carried done by displaying the
available housing properties on a machine hackathon platform
and taking into account the data set that is still accessible to the
public. Our conclusion is that machine learning offers a
promising, alternative technique in property valuation, appraisal
research, especially in relation to property price prediction, The
goal is to develop a predictive model for estimating price based
on price-influencing parameters |
Cite this article: |
Dr. Dayanand G. Savakar, *Kirthi Galgali, Danesh Telsang , "
Automated tool for the Prediction of House Price Using Machine Learning Techniques." , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 11, Issue 5, September - October 2022 , pp.
001-008 , ISSN 2278-6856.
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