The study "Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning" by MD Rokibul Hasan, Rejon Kumar Ray, and Faizah Rahat Chowdhury explores the integration of business analytics and machine learning to forecast employee performance. The authors highlight the importance of accurate performance prediction in human resource management, emphasizing the limitations of traditional subjective evaluation methods. They propose a consolidated approach that leverages data from various sources, including performance metrics, staff data, and contextual factors, to develop accurate predictive models. The research objectives include identifying key variables affecting employee performance, using business analytics to extract meaningful features, developing predictive models with machine learning algorithms, and evaluating the practicality and effectiveness of the integrated approach. The study employs methods such as descriptive analytics, prescriptive analytics, predictive modeling, natural language processing, and cluster analysis to enhance the accuracy of performance predictions. The integration of business analytics and machine learning is discussed in detail, covering data collection, preprocessing, profiling, feature selection, model selection, training and testing, model optimization, and deployment and monitoring. The authors conclude that this integrated approach significantly improves the accuracy of employee performance predictions, aiding companies in making informed decisions about talent management and resource allocation. However, continuous monitoring and adjustment are essential to maintain the model's performance over time.The study "Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning" by MD Rokibul Hasan, Rejon Kumar Ray, and Faizah Rahat Chowdhury explores the integration of business analytics and machine learning to forecast employee performance. The authors highlight the importance of accurate performance prediction in human resource management, emphasizing the limitations of traditional subjective evaluation methods. They propose a consolidated approach that leverages data from various sources, including performance metrics, staff data, and contextual factors, to develop accurate predictive models. The research objectives include identifying key variables affecting employee performance, using business analytics to extract meaningful features, developing predictive models with machine learning algorithms, and evaluating the practicality and effectiveness of the integrated approach. The study employs methods such as descriptive analytics, prescriptive analytics, predictive modeling, natural language processing, and cluster analysis to enhance the accuracy of performance predictions. The integration of business analytics and machine learning is discussed in detail, covering data collection, preprocessing, profiling, feature selection, model selection, training and testing, model optimization, and deployment and monitoring. The authors conclude that this integrated approach significantly improves the accuracy of employee performance predictions, aiding companies in making informed decisions about talent management and resource allocation. However, continuous monitoring and adjustment are essential to maintain the model's performance over time.