Housing price prediction using sequence transformers
The objective of this project is to create a forecast of Singapore’s housing prices using a dataset that includes prices of Housing and Development Board (HDB) flats over 5-10 years. The machine learning technique used in this research will be Sequence Transformers which is often used in Natural Lan...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The objective of this project is to create a forecast of Singapore’s housing prices using a dataset that includes prices of Housing and Development Board (HDB) flats over 5-10 years. The machine learning technique used in this research will be Sequence Transformers which is often used in Natural Language Processing (NLP). The paper applies the multi-layer attention layer, which improves processing time by parallelizing input data. The Transformer model allows for a bigger dataset to be used as compared to Recurrent Neural Network (RNN) tools such as Long-Short Term Memory (LSTM). Therefore, this project aims to test the feasibility of using Sequence Transformers by validating the output with loss functions by comparing training loss to validation loss. |
|---|