Predicting Employee Churn in Python

Predicting Employee Churn in Python

Author Name : Ankit Chandola, Nikhil Garg, Paras Vohra, Nishant Sharma, Shimpy Goyal Asst. Professor

SUMMARY

Employee churn can be defined as the leaking or departure of intellectual property from a company or organization. Alternatively, in simple terms, you could say, when employees leave an organization known as a churn. Another explanation would be when a member of the public leaves the community, known as a churn.

In the study, it was found that the churn of employees would be affected by age, occupancy, pay, job satisfaction, salary, working conditions, growth potential and employee perceptions of fairness. Other variables, such as age, gender, race, education, and marital status, were important factors in the forecasting of the employee fund. In some cases such as a skilled niche worker it is difficult to change. It affects the ongoing performance and productivity of existing staff. Finding new employees to replace we have costs such as hiring and training costs. Also, a new employee will take the time to learn skills at the same level of technical or business knowledge of an older employee. Organizations deal with this problem by using electronic learning methods to predict the outbreak of workers, which helps them to take necessary action.

Keywords- Employee Churn, Exploratory data analysis, Data visualization, constructing prediction model, model performance test