Analysis of Factors Affecting the Annual Salary of Data Analysts Based on Random Forest Regression Model

With the trend of the big data industry penetrating into various industries, the world’s demand for data analysts is increasing day by day. However, there is a phenomenon in a society where salary does not match resume-related factors (e.g., resume). Therefore, this article chooses to use the random forest regression model to analyze the factors affecting the annual salary of data analysts. Through descriptive statistics and model construction, it aims to obtain the most significant influencing factors from many related factors. The research results show that factors such as age, experience of machine learning, computing platform, place of residence, and the service expenses of machine learning and cloud have significant effects. In addition, the impact of academic qualifications on salary is not obvious, and job seekers should improve their most essential data analysis skills. Finally, this article provides corresponding training and recruitment strategies for schools and companies that have a need for data analysts.