| Summary: | 碩士 === 國立交通大學 === 資訊學院資訊學程 === 102 === Information for buying a house is from friends, real-agent-web and register-real-price data for most people. However ,those data are in different places.There are no direct comparison. I build a model by using register-real-price data of Hsinchu County.First ,I observe data and delete irrelevant data.For example ,I delete office buildings for commercial purposes. Second,I use K-means clustering to get the conclusion.The average price of real-agent-web is higher than the average price of register-real-price .Third,I calculate ratios of real-agent-web 's price to register-real-price's price by the conditions of 「square feet 」and「age of building」. Fourth,I find some real instances to support the experiment.Fifth,I install Apache and MySQL in Ubuntu and write HTML and PHP. I use the UTF-8 character set to process Chinese words in the house-price data. I write a shell script.It can get data from data.gov.tw and tw.house.yahoo.com termly and automatically. I write Python code to process data.The program imports them to the database automatically.I apply for a web space in order to provide the service that analyzes house prices.The system compares the house-price information of real-agent-web and register-real-price in same counties,「square feet」and「age of building」. It also shows mean and standard deviation of price's ratios.I use 「Google Analytics 」to observe user's browsing behavior .I get users' feedback by questionnaires. In conclusion,the analysis of house prices is useful for consumers.
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