Digital Repository

House Price Prediction Using Machine Learning

Show simple item record

dc.contributor.author Chandramohan, Kavyavarshaa
dc.date.accessioned 2022-12-20T05:16:52Z
dc.date.available 2022-12-20T05:16:52Z
dc.date.issued 2022
dc.identifier.citation Chandramohan, Kavyavarshaa (2022) House Price Prediction Using Machine Learning. BEng. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 2018164
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/1190
dc.description.abstract This paper presents an overview of how to predict home expenses using several variables such as the property's area, floor space, and number of levels, among others. To get the most efficient and less error-caused regression approach, an analysis is done using machine learning regression techniques such as Random Forest Regression, Linear regression, Gradient Boost and others. Based on the results of the investigation, it has been established that the Random Forest Regression Method outperforms other methods. The proposed approach took into account the most detailed components of the house price calculation and provided a rather more accurate prediction. en_US
dc.language.iso en en_US
dc.subject Machine Learning en_US
dc.subject Gradient Boost en_US
dc.subject Random Forest en_US
dc.subject Linear Regression en_US
dc.title House Price Prediction Using Machine Learning en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search


Advanced Search

Browse

My Account