Comprehensive Study on Employee Promotion Using Classification Techniques
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Abstract: Promoting employees is an essential procedure in organizational frameworks that directly affects motivation, productivity, and retention of the workforce. This research investigates data-centric approaches for forecasting employee advancements through classification methods. A collection of 1,000 employee records was examined, featuring variables with 12 like education, age, training scores, last year’s rating, and department. Following preprocessing to manage absent values and encode categorical variables, models such as Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) were created and assessed. Performance metrics including accuracy, precision, recall, specificity, and F1 score were utilized to evaluate model results. GNB proved to be the best model, achieving an accuracy of 83% on the test data, demonstrating resilience despite class imbalance. The study finds that statistical learning methods can greatly assist human resource departments in making informed, fair, and efficient decisions regarding promotions.
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