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Predicting Real-Estate valuation using Random Forest Regression.

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dc.contributor.author Zihan, Armanul Habib
dc.contributor.author Zabin, Musharrat
dc.contributor.author Taherin, Raihana
dc.date.accessioned 2022-04-26T07:48:57Z
dc.date.available 2022-04-26T07:48:57Z
dc.date.issued 2020-06-22
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3514
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract Prediction models in real estate have a significant role to play in telling the future of the real estate industry. They have a role to play in forecasting, which is important for investors who use the knowledge to make successful decisions. We propose a methodology using a combination of Machine learning (Random Forests), Graphic Information and different regression models. Examining real estate valuation helps to understand where people tend to live in a city. Predicting real estate valuation can help the urban design and urban politics, as it could help identify what factors have the most impact on property prices. Spot checking algorithms helped us identify a candidate to model our issue and test rapidly different regression models using spot checking. By applying this methods, have found a model that help us predict the house price of unit area in Xindian district, New Taipei, Taiwan. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00227
dc.subject Forest regression, algorithm, machine learning. en_US
dc.title Predicting Real-Estate valuation using Random Forest Regression. en_US
dc.type Thesis en_US


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