A Review Predicting Worker Happiness in the Workplace, Predicting Whether or not an Employee Will Stay with the Company: A New and Insightful Way to Use Machine Learnin G (MATLAB)
The company's employees are its most valuable asset. A company's fate is tied to the individuals that make up its workforce. Challenges arise for firms when skilled workers leave to pursue better chances elsewhere. The study's goal was to learn how dissatisfied workers are and why they would consider looking for a new job. After the root cause(s) of employee unhappiness has been identified, corrective measures may be implemented to hopefully reduce turnover. Based on the Employee dataset available on the Kaggle platform, this study aims to create a system that can predict employee turnover. With the use of a heatmap, we were able to see how each characteristic was connected to each other. We used KNN (K-Nearest Neighbor), SVM (Support Vector Machine), Decision Tree, and Random Forest (among others) to make predictions using machine learning. In this research, we explore the factors that contribute to employee turnover in each given business.
Keywords: Attrition Rate, Classifier, pre-processing, Employment Features, Acronyms: RF, SVM, KNN, ART, HR, Classifier, Pre-processing, Feature Selection, Attrition Rate, K-Closest Neighbor.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
International Journal of Engineering Technology and Computer Research (IJETCR) by Articles is licensed under a Creative Commons Attribution 4.0 International License.