An innovative data mining approach to predicting employee performance in dynamic workplaces

The analysis of various employee-related data, including work habits, talents, and behavior patterns, is required to predict their performance in dynamic work contexts. This creative method of predicting employee performance makes use of machine learning (ML) to find patterns and trends that affect employee success. Businesses can make well-informed decisions on developing training and careers, using machine learning (ML) algorithms to analyze giant datasets and find micro correlations between different elements. Adjusting to the changing conditions of the workplace helps the firms to improve the future capacity of workforce management, employee productivity, and overall corporate performance. The purpose is to uncover important determinants of employee success to improve performance outcomes and productivity. This research suggests a novel Intelligent Penguin Optimized Dynamic Random Forest (IPO-DRF) for predicting employee performance in dynamic workplaces. The employee performance data were collected, and it is crucial for improving ML approaches for the performance of employees in changing workplaces. The gathered data is preprocessed using Z-score normalization and Independent Component Analysis (ICA) is utilized for feature extraction. It is difficult to forecast employee performance in dynamic workplaces because of shifting work environments and random elements like motivation and interpersonal interactions, which reduces the accuracy of previous data. The Python platform was employed in this research. The outcome displays the IPO-DRF suggested approach has improved forecasting worker performance in changing environments with the greatest accuracy (96.76%), precision (0.92), recall (0.7503), and F1- score (0.9534). This research shows that an inventive data mining technique can forecast employee performance in dynamic workplaces with high accuracy, improving organizational efficiency and decision-making. The model's accuracy demonstrates how well it can optimize personnel management tactics in settings that are changing quickly.