Classification Model for Effective Employee Segmentation

Abstract

In this work, an efficient classification model for staff segmentation is developed. The ensemble is based on machine learning principles, allowing the exploration of the performance of various classification methods and the tuning of hyperparameters to optimize system performance. Additionally, it provides the ability to compare the metric results of trained models, enabling the selection of the best strategy for each problem. The work considers an efficient and automated data processing pipeline, which includes data collection, cleaning, and transformation processes that can be applied in various fields where efficient data processing is required.