Using data from the Human Resources department at IBM, I created an EDA on reasons why attrition occurred.
Employee attrition costs U.S businesses about $1 trillion every year according to Gallup research. The reasons for employee attrition can range from voluntary attrition, which means the employee can leave the company for any reason on their own terms, and involuntary attrition, which means that the employee has been let go for any number of reasons by the company. According to the bureau of labor statistics, it can cost companies one and half to two times the annual salary for replacing that individual employee. In this analysis I would like to find some factors that might explain why employees leave the company especially at IBM. Are there specific jobs that have more turnover than others? What trend can I find between job satisfaction and job compensation? These questions piqued my curiosity for exploratory data analysis. Companies want to work as efficiently as possible and make the most amount of profit. I was curious to find that companies lose a lot of money in the costs of employee attrition. Finding patterns to solve this problem for IBM can potentially save them millions or at the least one and half to two times their employees salary for those that have left the company.
The data was taken from Kaggle by the user pavansubhash. The Human Resources department compiled this data and pavansubhash provided this data as a .csv file.