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...
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2022
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sg-ntu-dr.10356-1575382023-07-07T19:16:57Z Housing price prediction using sequence transformers Muhammad Aidil Goh Jalil Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-19T08:03:00Z 2022-05-19T08:03:00Z 2022 Final Year Project (FYP) Muhammad Aidil Goh Jalil (2022). Housing price prediction using sequence transformers. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157538 https://hdl.handle.net/10356/157538 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Muhammad Aidil Goh Jalil Housing price prediction using sequence transformers |
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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. |
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Soh Yeng Chai |
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Soh Yeng Chai Muhammad Aidil Goh Jalil |
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Final Year Project |
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Muhammad Aidil Goh Jalil |
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Muhammad Aidil Goh Jalil |
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Housing price prediction using sequence transformers |
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Housing price prediction using sequence transformers |
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Housing price prediction using sequence transformers |
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Housing price prediction using sequence transformers |
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Housing price prediction using sequence transformers |
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housing price prediction using sequence transformers |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/157538 |
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