Forecasting Multan estate prices using optimized regression techniques
Purchasing a house or plot has become a complicated task for an average person due to budget constraints and market situations. An individual does not know the prices of the plots and gets trapped by a middle man. This paper proposes a solution for this problem by predicting the plot prices using a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Sindh
2022-04-01
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| Series: | University of Sindh Journal of Information and Communication Technology |
| Subjects: | |
| Online Access: | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/4340 |
| _version_ | 1797805646005927936 |
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| author | Adnan Akhter Muhammad Bux Alvi Majdah Alvi |
| author_facet | Adnan Akhter Muhammad Bux Alvi Majdah Alvi |
| author_sort | Adnan Akhter |
| collection | DOAJ |
| description | Purchasing a house or plot has become a complicated task for an average person due to budget constraints and market situations. An individual does not know the prices of the plots and gets trapped by a middle man. This paper proposes a solution for this problem by predicting the plot prices using a machine learning approach, leveraging Multiple Linear Regression, Gradient Boosting Regression, and Random Forest regression techniques. This work compares the performance of these three algorithms by hyper-parameter tuning using gird search, and random search for checking which one is adequate in terms of scores and error rates. Factors that influence the prices of the plots include plot covered area, physical condition of the plot, area, and population. Gradient boosting regression has surpassed all other machine learning methods, achieving the lowest error rates and highest R-squared score of 0.987 with grid search. The resultant predictive systems can help the folk in three ways. 1) safety from deception 2) budget oriented instant information, and 3) time saving. |
| first_indexed | 2024-03-13T05:55:11Z |
| format | Article |
| id | doaj.art-bd3744be778843eeae60bf1deb1e4301 |
| institution | Directory Open Access Journal |
| issn | 2521-5582 2523-1235 |
| language | English |
| last_indexed | 2024-03-13T05:55:11Z |
| publishDate | 2022-04-01 |
| publisher | University of Sindh |
| record_format | Article |
| series | University of Sindh Journal of Information and Communication Technology |
| spelling | doaj.art-bd3744be778843eeae60bf1deb1e43012023-06-13T06:16:57ZengUniversity of SindhUniversity of Sindh Journal of Information and Communication Technology2521-55822523-12352022-04-01541661734340Forecasting Multan estate prices using optimized regression techniquesAdnan Akhter0Muhammad Bux Alvi1Majdah Alvi2The Islamia University of BahawalpurThe Islamia University of BahwalpurThe Islamia University of BahwalpurPurchasing a house or plot has become a complicated task for an average person due to budget constraints and market situations. An individual does not know the prices of the plots and gets trapped by a middle man. This paper proposes a solution for this problem by predicting the plot prices using a machine learning approach, leveraging Multiple Linear Regression, Gradient Boosting Regression, and Random Forest regression techniques. This work compares the performance of these three algorithms by hyper-parameter tuning using gird search, and random search for checking which one is adequate in terms of scores and error rates. Factors that influence the prices of the plots include plot covered area, physical condition of the plot, area, and population. Gradient boosting regression has surpassed all other machine learning methods, achieving the lowest error rates and highest R-squared score of 0.987 with grid search. The resultant predictive systems can help the folk in three ways. 1) safety from deception 2) budget oriented instant information, and 3) time saving.https://sujo.usindh.edu.pk/index.php/USJICT/article/view/4340regression methods, machine learning, plot prediction, hyper parameter tuning |
| spellingShingle | Adnan Akhter Muhammad Bux Alvi Majdah Alvi Forecasting Multan estate prices using optimized regression techniques University of Sindh Journal of Information and Communication Technology regression methods, machine learning, plot prediction, hyper parameter tuning |
| title | Forecasting Multan estate prices using optimized regression techniques |
| title_full | Forecasting Multan estate prices using optimized regression techniques |
| title_fullStr | Forecasting Multan estate prices using optimized regression techniques |
| title_full_unstemmed | Forecasting Multan estate prices using optimized regression techniques |
| title_short | Forecasting Multan estate prices using optimized regression techniques |
| title_sort | forecasting multan estate prices using optimized regression techniques |
| topic | regression methods, machine learning, plot prediction, hyper parameter tuning |
| url | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/4340 |
| work_keys_str_mv | AT adnanakhter forecastingmultanestatepricesusingoptimizedregressiontechniques AT muhammadbuxalvi forecastingmultanestatepricesusingoptimizedregressiontechniques AT majdahalvi forecastingmultanestatepricesusingoptimizedregressiontechniques |