IBM Employee Attrition Analysis

Shenghuan Yang, Pradeep Ravikumar, Timothy Shi

In this paper, we analyzed the dataset IBM Employee Attrition to find the main reasons why employees choose to resign. Firstly, we utilized the correlation matrix to see some features that were not significantly correlated with other attributes and removed them from our dataset. Secondly, we selected important features by exploiting Random Forest, finding monthlyincome, age, and the number of companies worked significantly impacted employee attrition. Next, we also classified people into two clusters by using K-means Clustering. Finally, We performed binary logistic regression quantitative analysis: the attrition of people who traveled frequently was 2.4 times higher than that of people who rarely traveled. And we also found that employees who work in Human Resource have a higher tendency to leave.

Knowledge Graph



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