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Title: Housing price prediction using sequence transformers
Authors: Muhammad Aidil Goh Jalil
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Muhammad Aidil Goh Jalil (2022). Housing price prediction using sequence transformers. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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