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Using Machine Learning Classifiers and A Virtual Voice Assistant for Common Tasks, An Employee Performance Evaluation Model is Used

This study focuses on a conversational voice assistant for businesses that facilitates staff productivity and efficiency gains by speeding up routine, time-consuming operations. By using straightforward voice commands, they can manage conference rooms, arrange meetings, and adjust work hours thanks to speech recognition and TTS technologies. The necessity of employee performance analysis is also taken into account in this study, and this is done with the aid of machine learning classification models, specifically L1 and L2 Regularization, Random Forest, and Gradient Boost. The experimental results reveal that the Random Forest Classification Algorithm yields the highest accuracy, F1 Score, Train and Test AUC-ROC, with values of 97.08 percent, 98.27 percent, 99.82 percent, and 98.41 percent, respectively.