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Ogunbiyi, Oyindamola Eniola (2020) House Sale Price Prediction using Feature Engineering Techniques and Ensemble Learning Algorithms. Masters thesis, Dublin, National College of Ireland.
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Abstract
Housing is one of the fundamental essential of every living thing hence the reason for continuous research in this sector. This project simply examines a dataset, which consists 1460 observations and 80 features that contribute to the sale price of the houses. Dataset was cleaned and transformed and some explorations were done on it to answer some basic questions that anybody would like to ask about housing. Feature engineering was performed on the transformed data using Principal Component Analysis (PCA) and dummy encoding and this is to ensure our dataset is ready in the right form with the right variables to be used in the algorithms, which results in improved model accuracy. Different ensemble algorithms were used on the dataset in this project. The overall result of this project shows that the most important variables that determine the price of a house being sold.
Keywords: Housing price, Principal Component Analysis, Dummy encoding, Ensemble Algorithms and Feature Engineering.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HD Industries. Land use. Labor > Housing |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Dan English |
| Date Deposited: | 23 Jun 2020 10:53 |
| Last Modified: | 23 Jun 2020 10:53 |
| URI: | https://norma.ncirl.ie/id/eprint/4314 |
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