House Prices Prediction System Based on Open Government Data
| Autor: | Liang, Chih-Pin, 梁志彬 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 It is a great expense for most people to buy the house. Thus, it is necessary to assess the information related to house carefully, especially the cost. If we can predict the house prices before buying it, it would be the important information for home buyers. They can decide whether to buy the house and bargain house price according to the prediction result. With the popularity of open data, the situation of information asymmetry between buyers and sellers disappears gradually. The service via data analysis and data visualization emerges in recent years. Most services analyze the data and present the result by plot rather than predict house price. The prediction methods are simpler in existing house prices prediction service. They merely use statistics or simple machine learning models to predict house prices. This thesis proposed the house prices prediction system for service providers. They can provide better service to home buyers by predicting more precise result through our system. User can select the prediction method and get prediction result from GUI. Unlike past analytical method we cluster data, make the prediction for each cluster and integrate all results. Besides, the system can preserve the analytical result. It is an useful feature when new transaction data are generated. The time of house prices prediction would be reduced dramatically because we only need to analyze new data rather than historical data. By predicting the past known house prices, we can know that the system can indeed predict house prices precisely. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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