| Summary: | 碩士 === 國立臺北科技大學 === 電子工程系 === 106 === With the advent of the era of big data, more and more examples of AI-related technologies are used in life. Neural networks can be combined with applications at various level, such as semantic analysis and image recognition, etc. In this thesis, we apply neural networks to the prediction of foreclosure house prices, which makes it easier to analyze the price.
The proposed platform consists of four parts: data collection, neural networks, back end and front end. The data are collected by controlling Chrome Browser with Selenium WebDriver. For the neural networks part, Keras Library is used to set up and train the network. With regard to the back end technology, ASP.NET WebAPI framework is used to deal with the connection between the front end and the database. Finally, ReactJs is applied to develop the front end technology.
The proposed foreclosure houses information platform collects data on foreclosure houses and auctions based on web crawler. After rearranging the data, the system then inputs the collected information to the Neural network for training and price prediction. The information includes the cities and the districts where the houses locate, the size of the houses, land rights, dates of auction, whether final walk-through has been undergone, the maximum and the minimum of the house prices, and types of land weight. The final results are presented on the website and serve as an index in analyzing the house prices.
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