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 |