Overview of machine learning methods in predicting house prices and its application in R
| dc.contributor.advisor | Keitt, Timothy H. | |
| dc.contributor.committeeMember | Viswanathan, Bindu | |
| dc.creator | Chen, Wei-Ta | |
| dc.date.accessioned | 2018-03-07T20:09:08Z | |
| dc.date.available | 2018-03-07T20:09:08Z | |
| dc.date.created | 2017-12 | |
| dc.date.issued | 2018-01-25 | |
| dc.date.submitted | December 2017 | |
| dc.date.updated | 2018-03-07T20:09:09Z | |
| dc.description.abstract | This report aims to predict house prices by using several machine learning methods. These methods include ordinary least squares regression, Ridge regression, Lasso regression, and k-nearest neighbor regression. We compare the prediction accuracy by using root mean square error (RMSE) among these models to determine which model performs best in the predictions of house price. The propose of this report is to give an overview of how to perform different models in predicting house prices and its implementation in R. | |
| dc.description.department | Statistics | |
| dc.format.mimetype | application/pdf | |
| dc.identifier | doi:10.15781/T2125QS5C | |
| dc.identifier.uri | http://hdl.handle.net/2152/63804 | |
| dc.language.iso | en | |
| dc.subject | Machine learning | |
| dc.subject | Regression | |
| dc.subject | RMSE | |
| dc.title | Overview of machine learning methods in predicting house prices and its application in R | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Statistics | |
| thesis.degree.discipline | Statistics | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Statistics |
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