Housing Price Prediction with Machine Learning
Amena Begum1, Nishad Jahan Kheya2, Md. Zahidur Rahman3

1Amena Begum, Department of ICT, Comilla University, Cumilla , Bangladesh. 
2Nishad Jahan Kheya, Department of ICT, Comilla University, Cumilla, Bangladesh. 
3Md. Zahidur Rahman*, Department of CSE, Britannia University, Cumilla, Bangladesh. 
Manuscript received on January 20, 2022. | Revised Manuscript received on January 01, 2022. | Manuscript published on February 28, 2022. | PP: 42-46 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97410111322 | DOI: 10.35940/ijitee.C9741.0111322
Open Access | Ethics and  Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: For socioeconomic development and the well-being of citizens, developing a precise model for predicting housing prices is always required. So that, a real estate broker or a house seller/buyer can get an intuition in making well-knowledgeable decisions from the model. In this work, a various set of machine learning algorithms such as Linear Regression, Decision Tree, Random Forest are being implemented to predict the housing prices using available datasets. The housing datasets of 506 samples and 13 feature variables from January 2015 to November 2019 were taken from the StatLib library which is maintained at Carnegie Mellon University. Since housing price is emphatically connected to different factors like location, area, the number of rooms; it requires all of this information to predict individual housing prices. This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models to explore various impacts of features on prediction methods. This paper will also provide an optimistic result for housing price prediction by comprehensively validating multiple techniques in model execution on regression. 
Keywords: Prediction, Machine Learning, Linear Regression, Random Forest, Decision Tree.
Scope of the Article: Machine Learning