Explore Machine Learning using Python When an employee quits the organization, they take way experience, skill, knowledge acquired over a period of time within the organization. This affects the organization and the impact is not only restricted to that but also brings the task of finding a suitable replacement. Mostly the suitable replacement is hired from external and it again adds time and cost to the organization.
The HR department of a multinational company would like to understand the reasons for premature exit of experienced employees using Machine Learning techniques. For achieving this, they must:
Explore the dataset and check if the data can be used as-is. Determine the relationship between satisfaction level and working hours of employees who have left the organization. Understand the effect of satisfaction level, department, promotion in last 5 years and salary level of employees who have left the organization. Build a machine learning model to predict the exit of employees.
The dataset has roughly 15000 records with10 columns, which are self-explanatory, namely: satisfaction_level, last_evaluation, number_project, average_monthly_hours, time_spend_company, Work_accident, left, promotion_last_5years, Department, salary.