Housing price prediction using feedforward neural networks
House price prediction is an essential tool in the housing market and basis for any decision making in order to maximize the benefits. This project uses an artificial neural network to develop a prediction model for housing prices in the Housing Development Board (HDB) resale market in Singapore. Th...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/136839 |
| _version_ | 1811691515010875392 |
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| author | Nadiah Ishak |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Nadiah Ishak |
| author_sort | Nadiah Ishak |
| collection | NTU |
| description | House price prediction is an essential tool in the housing market and basis for any decision making in order to maximize the benefits. This project uses an artificial neural network to develop a prediction model for housing prices in the Housing Development Board (HDB) resale market in Singapore. This study will also identify important determinants that will affect HDB resale prices. With the identified price determinants, the information will be feed into a neural network model for training, testing, and validation. The training models used for this study are the Decision Tree Model, Levenberg-Marquardt Algorithm and Stochastic Gradient Descent. Experiment results support the notion that an artificial neural network approach is a suitable tool as they are able to map out the interactions between different determinants used. |
| first_indexed | 2024-10-01T06:21:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/136839 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:21:07Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1368392023-07-07T17:08:43Z Housing price prediction using feedforward neural networks Nadiah Ishak Wang Lipo School of Electrical and Electronic Engineering elpwang@ntu.edu.sg Engineering::Electrical and electronic engineering House price prediction is an essential tool in the housing market and basis for any decision making in order to maximize the benefits. This project uses an artificial neural network to develop a prediction model for housing prices in the Housing Development Board (HDB) resale market in Singapore. This study will also identify important determinants that will affect HDB resale prices. With the identified price determinants, the information will be feed into a neural network model for training, testing, and validation. The training models used for this study are the Decision Tree Model, Levenberg-Marquardt Algorithm and Stochastic Gradient Descent. Experiment results support the notion that an artificial neural network approach is a suitable tool as they are able to map out the interactions between different determinants used. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-01-31T02:13:15Z 2020-01-31T02:13:15Z 2019 Final Year Project (FYP) https://hdl.handle.net/10356/136839 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Nadiah Ishak Housing price prediction using feedforward neural networks |
| title | Housing price prediction using feedforward neural networks |
| title_full | Housing price prediction using feedforward neural networks |
| title_fullStr | Housing price prediction using feedforward neural networks |
| title_full_unstemmed | Housing price prediction using feedforward neural networks |
| title_short | Housing price prediction using feedforward neural networks |
| title_sort | housing price prediction using feedforward neural networks |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/136839 |
| work_keys_str_mv | AT nadiahishak housingpricepredictionusingfeedforwardneuralnetworks |