Employee Attrition Prediction Using Logistic Regression and Selectkbest: A Case Study on The IBM HR Analytics Employee Attrition and Performance Dataset
Keywords:
Machine learning, Logistic Regression, SelectKBest, ANOVA F-TestAbstract
Employee attrition is a significant strategic concern for organizations as it directly impacts overall performance, productivity, and long-term sustainability. High attrition rates can lead to increased costs in recruitment and training, a loss of skilled and experienced employees, decreased morale among remaining staff, and disruptions to critical business operations. In response to these challenges, many organizations are turning to predictive analytics to anticipate employee turnover and implement effective retention strategies. This study proposes a machine learning-based approach to predict employee attrition using the Logistic Regression algorithm. Logistic Regression is chosen due to its effectiveness in binary classification tasks and its interpretability, which is essential for human resource (HR) professionals when making data-driven decisions. To enhance the model’s performance, the SelectKBest feature selection technique is applied in conjunction with the ANOVA F-test. This method allows the identification of the most influential features contributing to attrition, helping reduce noise and computational complexity while improving model accuracy. The IBM HR Analytics Employee Attrition & Performance Dataset is used in this study. The dataset contains a variety of demographic and organizational attributes such as age, monthly income, job role, tenure, and job satisfaction. The data undergoes a comprehensive preprocessing phase that includes numerical transformation, encoding of categorical variables, normalization, and the implementation of feature selection. By combining Logistic Regression with effective feature selection, this research aims to deliver an accurate and interpretable predictive model. The results are expected to help HR departments proactively identify high-risk employees and take strategic actions to reduce attrition, ultimately supporting better workforce planning and organizational stability.
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