Comparison of Classification Algorithms and Undersampling Methods on Employee Churn Prediction: A Case Study of a Tech Company

<p>Churn prediction is a common data mining problem that many companies face across industries. More commonly, customer churn has been studied extensively within the telecommunications industry where there is low customer retention due to high market competition. Similar to customer churn, employee churn is very costly to a company and by not deploying proper risk mitigation strategies, profits cannot be maximized, and valuable employees may leave the company. The cost to replace an employee is exponentially higher than finding a replacement, so it is in any company’s best interest to prioritize employee retention.</p> <p>This research combines machine learning techniques with undersampling in hopes of identifying employees at risk of churn so retention strategies can be implemented before it is too late. Four different classification algorithms are tested on a variety of undersampled datasets in order to find the most effective undersampling and classification method for predicting employee churn. Statistical analysis is conducted on the appropriate evaluation metrics to find the most significant methods.</p> <p>The results of this study can be used by the company to target individuals at risk of churn so that risk mitigation strategies can be effective in retaining the valuable employees. Methods and results can be tested and applied across different industries and companies.</p>

Name thesis:degree_name

MS in Industrial Engineering

Discipline thesis:degree_discipline

Industrial and Manufacturing Engineering

Year dc:date.available

2020

Author dc:creator

• Cooper, Heather

Contributors dc:contributor

• Tali Freed
• Industrial and Manufacturing Engineering
• College of Engineering

• Data Mining
• Machine Learning
• Unbalanced Dataset
• Industrial Engineering
• Operations Research, Systems Engineering and Industrial Engineering

Identifier

10.15368/theses.2020.153

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3753