  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.
