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dc.contributor.authorMuhammad Aidil Goh Jalilen_US
dc.identifier.citationMuhammad Aidil Goh Jalil (2022). Housing price prediction using sequence transformers. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThe 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.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleHousing price prediction using sequence transformersen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSoh Yeng Chaien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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