Application Research of Naive Bayes Classification Algorithm in Weather Website

EasyChair Preprint no. 5685, version 3

Abstract

The generation and update of weather.net travel products provide tourists with a reference for the weather conditions in the destination. However, for various reasons, weather.net travel products cannot be updated on time. Manual monitoring is required to update. If it is not updated, manual update is required, which undoubtedly increases the business staff’s Burden, it is necessary to find a time-saving and labor-saving solution to solve this problem in the intelligent age. The DIKW (Data Information Knowledge Wisdom) model is the most basic model in the research of information management and knowledge management. The model includes the collection, storage, processing and application of data under the data management mode as its main management content. The naive Bayes algorithm is widely used because of its high classification accuracy and simple model. For this reason, by importing the model into specific meteorological application research, the naive Bayes classification prediction algorithm combined with Python crawler is applied to the weather network. For the update forecast of tourism products, the algorithm combines the historical update data mining of the weather network in the past month to calculate the prior probability, and then calculates its classification result according to the Python program to capture the data of the day. By recording the future 16 sample data sets using this model for calculation and analysis, 15 pieces of data conform to the results of the model calculation and classification, and the accuracy rate reaches 93.7%. The results show that the higher accuracy of the algorithm classification forecast can promptly remind business personnel, and better guarantee the timely update of tourism products, thereby improving the work efficiency of business personnel, and providing practical application reference value for the automation of weather service business.

Keyphrases: classification prediction, Crawler, meteorological, Naive Bayes, weather website

@Booklet{EasyChair:5685,
  author = {Chaoning Li and Liang Chen and Shenghong Wu and Yunyin Mo and Liying Chen},
  title = {Application Research of Naive Bayes Classification Algorithm in Weather Website},
  howpublished = {EasyChair Preprint no. 5685},

  year = {EasyChair, 2021}}