Employee turnover refers to the number or percentage of workers who leave an organization and are replaced by new employees.
According to the Center of American Progress it costs about $10,000 to replace a working earning $50,000 per year, i.e 20% of their income.
Replacing a high-level employee can cost multiple of that.
● Cost of off-boarding
● Cost of hiring (advertising, interviewing, hiring)
● Cost of onboarding a new person (training, management time)
● Lost productivity (a new person may take 1-2 years to reach the productivity of an existing person)
Measuring employee turnover can be helpful to employers that want to examine reasons for turnover or estimate the cost-to-hire for budgeting purposes. My goal is to build a model which can predict whether an employee leaves or not and determine the factors which affect his decision making
I’ll be following a typical data science pipeline, which is call “OSEMN” (pronounced awesome).
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Obtaining the data is the first approach in solving the problem. Here the dataset is from Kaggle.
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Scrubbing or cleaning the data is the next step. Here we check for missing values, combine columns and more.
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Exploring the data will follow right after and allow further insight of what our dataset contains.
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Modeling the data will tell us whether an employee will leave or not.
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INterpreting the data is last. What are the features which influence whether an employee will leave or not?
- If you have any questions, feel free to contact me.