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Research on the Risk Analysis of Cigarette Business Based on Data Mining Technology

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DOI: 10.23977/ferm.2023.060816 | Downloads: 24 | Views: 426

Author(s)

Senqiang Wang 1, Xiao Song 1

Affiliation(s)

1 School of Management, Shandong University of Technology, Zibo, 255000, China

Corresponding Author

Senqiang Wang

ABSTRACT

It has always been the focus of the tobacco industry to carry out the risk investigation of standardized tobacco management. At present, there are still large risk management problems in the links of brand supply, supply distribution of retail households, real cigarettes and acquisition cases, which destroys the coordination of cigarette sales market. With the help of big data, we can effectively identify and supervise illegal business risks, so as to reduce the occurrence of abnormal situations. Based on the data mining technology, this paper comprehensively uses the supervised sk-learn algorithm, extracts and transforms the data analysis index in the cloud pos system, and studies the abnormal data in the process of cigarette business. In addition, this constructs a risk analysis model for cigarette business, which promotes the data governance and risk control ability of the cigarette market, and is conducive to optimizing the overall image of the tobacco industry. 

KEYWORDS

Data mining, cigarette management, risk analysis, sk-learn algorithm

CITE THIS PAPER

Xirong Ai, A Whole-Process Exploration of the Evolution of Security Issues. Financial Engineering and Risk Management (2023) Vol. 6: 132-138. DOI: http://dx.doi.org/10.23977/ferm.2023.060816.

REFERENCES

[1] Guo X. C. Application of a naive Bayesian classification algorithm. Communication World, 2019, 26 (01): 241-242. 
[2] He X. N, Duan F. H. Linear regression case analysis based on the python. Microcomputer application, 2022, 38 (11): 35-37. 
[3] Li C. S. Construction of enterprise financial risk early warning model based on logistic regression method. Statistics and Decision-making, 2018, 34 (06): 185-188. 
[4] Li C. B. Based on the "risk-oriented" internal supervision mode of cigarette business. Chongqing and the World (academic edition), 2013. 
[5] Luo X., Ouyang Y. X, Xiong Z., Yuan, M. The k-nearest neighbor-based collaborative filtering algorithm was optimized by similarity support. Journal of Computer Science, 2010, 33 (08): 1437-1445. 
[6] Pi Y.C. Application of k-neighbor classification algorithm. Communication World, 2019, 26 (01): 286-287. 
[7] Qing S. Construction and analysis of the company's business risk measurement method. Accounting and Communication, 2013 (24): 123-125. 
[8] Shen M. Y, Han M., Du S. Y, Sun R., Zhang C. Y. Summary of integrated classification algorithms for data flow decision trees. Computer applications and software, 2022, 39 (09): 1-10. 
[9] Xu Z. Q, Li Y. Y, Wan Y. C, Hu L. F, Xu B. Y. Thinking and measures on risk management theory. Value Engineering, 2020, 39 (05): 25-26. 
[10] Zhang C. J. Decision tree algorithm for big data analysis. Computer Science, 2016, 43 (s1): 374-379 383. 
[11] Zhang R., Wang Y. B. Machine learning and its algorithms and development research. Journal of Communication University of China (Natural Science edition), 2016, 23 (02). 

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