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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Singapore
2020
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| Subjects: | |
| 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 |
| _version_ | 1848822817542373376 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:47:16Z |
| format | Conference or Workshop Item |
| id | ump-27517 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:47:16Z |
| publishDate | 2020 |
| publisher | Springer Singapore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-275172020-07-28T07:19:54Z 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. Springer Singapore 2020-07-20 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 (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 (Published) https://doi.org/10.1007/978-981-15-6025-5_19 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/27517/ 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 |