Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, Nanjing, China

Research Article

The Application Research of Machine Learning-based Statistical Analysis Model in the Era of Big Data

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  • @INPROCEEDINGS{10.4108/eai.15-12-2023.2345337,
        author={Lingda  Wang},
        title={The Application Research of Machine Learning-based Statistical Analysis Model in the Era of Big Data},
        proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China},
        publisher={EAI},
        proceedings_a={PMBDA},
        year={2024},
        month={5},
        keywords={big data era; machine learning; application advantage},
        doi={10.4108/eai.15-12-2023.2345337}
    }
    
  • Lingda Wang
    Year: 2024
    The Application Research of Machine Learning-based Statistical Analysis Model in the Era of Big Data
    PMBDA
    EAI
    DOI: 10.4108/eai.15-12-2023.2345337
Lingda Wang1,*
  • 1: University of Edinburgh
*Contact email: 1230538273@qq.com

Abstract

This paper discusses the importance of machine learning and its application in the statistical analysis of big data in the era of big data. First, the paper introduces the characteristics and challenges of the era of big data, and points out the problems faced by big data processing and analysis. Then, the application advantages of machine learning in the era of big data are expounded. Then, the basic principle and classification of the machine learning algorithm are introduced in detail. In terms of its application in big data statistical analysis, the application of machine learning in data preprocessing and cleaning, data analysis and feature extraction, and prediction and prediction evaluation are discussed. At the same time, it also discusses the privacy and security problems of machine learning in the era of big data, as well as the optimization and improvement of machine learning model. Through the research of this paper, it will further promote the development of machine learning in the era of big data, improve the efficiency of data analysis and utilization, and provide more accurate support for decision-making.