Leveraging Multi-Model Gradient Boosting Techniques for Workforce Enablement: A Transparent Framework for Task Management and Issue Resolution

In the rapidly shifting workplace environment, fostering an inspiring and dynamic atmosphere is essential for organizational success. This paper introduces an innovative Workforce Enhancement Platform designed to improve transparency, teamwork, and communication within the team. The platform employs state-of-the-art techniques for analyzing textual data, utilizing Gradient Boosting Machines (GBM) such as XGBoost, LightGBM, and CatBoost, to evaluate and categorize worker-submitted issues. It features a comprehensive data cleaning procedure to remove extraneous elements like markup and special symbols, followed by converting the processed text into actionable features using methods such as term frequency and inverse document frequency. Central to the system is the application of GBM algorithms, which detect and classify the emotional tone of the reports, distinguishing between positive and negative sentiments. Furthermore, the platform incorporates advanced data visualization tools to provide an in-depth analysis of model performance, including accuracy metrics, confusion matrices, and feature importance evaluations. By optimizing the identification and classification of workplace issues, the platform facilitates effective resolution, fostering a setting of innovation and teamwork. The main goal of the platform is to boost staff engagement and support by addressing challenges proactively, thereby promoting both individual growth and collective progress. This comprehensive approach ensures that team members’ feedback is acknowledged and valued, ultimately contributing to a more positive and productive organizational environment.