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Enrollment and HR Analytics

Project Description

Institutional attrition refers to the phenomenon of members of an organization leaving it over time - a costly challenge faced by many institutions. This work focuses on the problem of predicting attrition as an application of supervised machine learning for classification using summative historical variables. Raising the accuracy, precision, and recall of learned classifiers enables institutional administrators to take individualized preventive action based on the variables that are found to be relevant to the prediction that a particular member is at high risk of departure. This project focuses on using multivariate logistic regression on historical institutional data with wrapper-based feature selection to determine variables that are relevant to a specified classification task for the prediction of attrition.

Supervised inductive learning algorithms such as Logistic Regression, support vector machines (SVM), random forests, and Naive Bayes are applied to predict the attrition of individual employees based on a combination of personal and institution-wide factors. The results of each algorithm are compared to evaluate the predictive models for this classification task. From an applications perspective, once deployed, this model can be used by human capital services units of an employer to find actionable ways (training, management, incentives, etc.) to reduce attrition and potentially boost longer-term retention.

Keywords

machine learning, predictive analytics, attrition, supervised classification, hr analytics

Methods

Methods include predictive modeling (e.g., linear regression, ridge regression), model classification (e.g., logistic regression, random forest classification), and reinforcement learning for building decision support systems.

Current Team Members

  • Sindhu Velumula

Alumni

Data Sets

  • IBM HR Data Set
  • Student data provided by Kansas State University

Source Code

References

KDD Lab Publications

No publications yet.

Last updated by pozegov on Jul 6, 2023