Employee Attrition Analytics – once again!

There’s a law with the name “Joy’s law” which states “no matter who you are, most of the smartest people work for someone else”. Can we break this law through data analytics?

Employee attrition is a very critical problem for the Human Resources department. In this era of competition, it becomes imperative to understand factors leading to employee attrition and employee retention. Some of those factors could be obvious while the others could be hidden.

Can our data on employee attrition give insights into:

  • Why are people leaving the company?
  • Which segment of employees is leaving?
  • Where should we focus on?

Answers to some of these questions will help a CHRO take steps in the correct direction, improve employee morale and engagement to reduce attrition.

In this data analytics report (close to 40 pages) we take you through a methodical framework developed by us and deep dive into each steps understanding the data, visualizing it and seeing the factors influencing attrition.

We use R programming and Power BI to create a compelling data story.

Through this report we would:

  1. Explore employee attrition data through various statistical and visualization techniques
  2. Find out factors influencing attrition
  3. Create a model to predict attrition
  4. Provide final conclusions

Our data analytics framework has 4 major steps:

  1. Data Exploration
  2. Distribution Analysis with respect to the variable of interest
  3. Model Development
  4. Model Analysis and Conclusion

Though we have applied this framework to employee attrition problem, the framework can be applied to any data analytics problems.

Language used: R

Tools used: R Studio, Power BI


Dataset: The dataset has been taken from IBM resource.

Link to the dataset: https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-HR-Employee-Attrition.xlsx

Thank you.

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