With the rapid development of tourism, more and more people choose to travel by air, and the price fluctuation of air tickets is large and the law is not significant, which makes it necessary to use some big data technical means to predict the change law of prices. The research and analysis of the characteristics of air tickets are helpful to analyze the internal value of tourism. Based on the web crawler technology, this paper obtains a large number of flight information from Shanghai to Guangzhou. Through data exploration and preprocessing, Random Forest classification model is established to predict the purchase of air tickets. The prediction accuracy of whether to buy or not is 96.96%. Then, ARIMA model is established to predict the ticket price trend. The deviation rate of the model is less than 18%. The future ticket price can be effectively predicted through Time Series. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets and technical support for the specified price strategy of the aviation industry, so as to promote the development of tourism.
The inverse matrix problem is a hot and active research topic in computational mathematics[1]. It has broad applications in engineering and scientific calculation, and owns a strong background in physics and practical significance[2]. This paper explores the inverse eigenvalue problem of a bordered anti-tridiagonal matrix. It first illustrates the existence and the uniqueness of its solution, the elaborates on the recursive expression of the solution and uses one numerical example to show the effectiveness of the algorithm, and finally concludes that this work is significant and points out suggestions for further study.
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