Automated tool for the Prediction of House Price Using Machine Learning Techniques.

Dr. Dayanand G. Savakar,  *Kirthi Galgali,  Danesh Telsang

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

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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