Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques

Flat price prediction is an important topic of real estate. Flat price in a city depends on different criteria such as, the crime rate of that location, total populations on that area, number of bedrooms, bathrooms, the total size of the flat, location of the flat, etc. People feel confused and face...

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Main Authors:	Jui, Julakha Jahan, Molla, M. M. Imran, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid
Format:	Conference or Workshop Item
Language:	English English
Published:	2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27517/ http://umpir.ump.edu.my/id/eprint/27517/1/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest1.pdf http://umpir.ump.edu.my/id/eprint/27517/2/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest.pdf

id	ump-27517
recordtype	eprints
spelling	ump-275172020-01-20T01:38:22Z http://umpir.ump.edu.my/id/eprint/27517/ Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques Jui, Julakha Jahan Molla, M. M. Imran Bari, Bifta Sama Rashid, Mamunur Hasan, Md Jahid TK Electrical engineering. Electronics Nuclear engineering Flat price prediction is an important topic of real estate. Flat price in a city depends on different criteria such as, the crime rate of that location, total populations on that area, number of bedrooms, bathrooms, the total size of the flat, location of the flat, etc. People feel confused and face different harassments with unreliable information during purchasing a flat in a city. By taking consideration of this scenario, we have proposed here flat price prediction framework. In this study, we have used our own data set that we have collected from Dhaka, Bangladesh. Two regression algorithms namely the linear regression and the regression tree/random forest regression have been used for building the prediction model. We have also checked the validity of the model using boxplot analysis, residual analysis, error checking and cross-validation. Finally, the performance of two methods has been compared which shows that the random forest regression model gives the best prediction result than the linear regression model. 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27517/1/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest1.pdf pdf en http://umpir.ump.edu.my/id/eprint/27517/2/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest.pdf Jui, Julakha Jahan and Molla, M. M. Imran and Bari, Bifta Sama and Rashid, Mamunur and Hasan, Md Jahid (2019) Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques. In: 11th Malaysian Technical Universities Conference on Engineering and Technology (MUCET 2019), 19-22 November 2019 , Kuantan, Pahang. pp. 1-14.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Jui, Julakha Jahan Molla, M. M. Imran Bari, Bifta Sama Rashid, Mamunur Hasan, Md Jahid Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
description	Flat price prediction is an important topic of real estate. Flat price in a city depends on different criteria such as, the crime rate of that location, total populations on that area, number of bedrooms, bathrooms, the total size of the flat, location of the flat, etc. People feel confused and face different harassments with unreliable information during purchasing a flat in a city. By taking consideration of this scenario, we have proposed here flat price prediction framework. In this study, we have used our own data set that we have collected from Dhaka, Bangladesh. Two regression algorithms namely the linear regression and the regression tree/random forest regression have been used for building the prediction model. We have also checked the validity of the model using boxplot analysis, residual analysis, error checking and cross-validation. Finally, the performance of two methods has been compared which shows that the random forest regression model gives the best prediction result than the linear regression model.
format	Conference or Workshop Item
author	Jui, Julakha Jahan Molla, M. M. Imran Bari, Bifta Sama Rashid, Mamunur Hasan, Md Jahid
author_facet	Jui, Julakha Jahan Molla, M. M. Imran Bari, Bifta Sama Rashid, Mamunur Hasan, Md Jahid
author_sort	Jui, Julakha Jahan
title	Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
title_short	Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
title_full	Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
title_fullStr	Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
title_full_unstemmed	Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques
title_sort	flat price prediction using linear and random forest regression based on machine learning techniques
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/27517/ http://umpir.ump.edu.my/id/eprint/27517/1/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest1.pdf http://umpir.ump.edu.my/id/eprint/27517/2/Flat%20Price%20Prediction%20using%20Linear%20and%20Random%20Forest.pdf
first_indexed	2023-09-18T22:43:17Z
last_indexed	2023-09-18T22:43:17Z
_version_	1777417057969635328

Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques

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