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Financial Data Analysis and Application Based on Big Data Mining Technology.

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This study explores data mining in big data for business management, proposing a weighted plain Bayesian and decision tree algorithm (WNB-C4.5) to analyze student performance and financial risks, enhancing accuracy by 2%.

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Area of Science:

  • Business Management
  • Data Mining
  • Big Data Analytics

Background:

  • Data mining technology is crucial in the big data era for business management.
  • Economic and technical feasibility of data mining in business contexts requires analysis.
  • Existing methods for analyzing student performance and financial risks can be improved.

Purpose of the Study:

  • To explore the characteristics and feasibility of data mining in business management.
  • To propose specific data mining applications for business management needs.
  • To develop an improved model for analyzing student performance and predicting financial risks.

Main Methods:

  • Overview of data mining in big data.
  • Analysis of economic and technical feasibility for business management.
  • Detailed description of weighted plain Bayesian and decision tree algorithms.
  • Combination of weighted plain Bayesian and C4.5 decision tree algorithms (WNB-C4.5 model).
  • Application of classification schemes for financial risk prediction.

Main Results:

  • The WNB-C4.5 model was developed for analyzing college students' learning literacy in physical education.
  • Decision trees are identified as a primary classification scheme for financial risk prediction and regulatory violation judgment.
  • Experimental results show a 2% increase in effectiveness for data mining in financial companies compared to benchmark methods.

Conclusions:

  • Data mining technology offers significant potential for enhancing business management practices.
  • The proposed WNB-C4.5 model demonstrates improved analytical capabilities for student performance.
  • Data mining, particularly decision trees, is effective in financial risk assessment and regulatory compliance.