Machine learning is a process in which computer is used to train and calculate input data and output results in a complex, multi task simulation. In data analysis, we can use machine learning to carry out experimental research and theoretical verification. In order to improve the ability of data analysis, we need to use machine learning and data mining methods to better process data. In this paper, experimental method and principal component analysis method are mainly used to test and discuss the fusion of machine learning in data analysis. The experimental results show that the CPU utilization rate in Scheme 4 is about 85% on average. The reason why the CPU of the Scribe center server is reduced is that after receiving data, there is less data to decompress, which reduces the CPU utilization.

Sujuan Han. Practice and Application of Fusion Machine Learning in Data Analysis. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 1-8. https://doi.org/10.38007/ML.2023.040101.

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