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

Jui, Julakha Jahan and Molla, M. M. Imran and Bari, Bifta Sama and Rashid, Mamunur and Hasan, Md Jahid (2020) 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. 205-217., 678. ISBN 978-981-15-6025-5

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Flat Price Prediction, Machine Learning, Linear Regression, Random Forest Regression.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 20 Jan 2020 01:38
Last Modified: 28 Jul 2020 07:19
URI: http://umpir.ump.edu.my/id/eprint/27517
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