# Data Analytics for Optimizing and Predicting Employee Performance
## 2 Background and Related Work  
Numerous researchers have investigated the models that can help us predict employee performance. We can identify the fact that, generally, an individual that supervises the employee or are their direct manager is the best person to conduct an employee performance evaluation [5]. It has also been determined that high levels of job satisfaction contribute to the increasing loyalty of the employee, a fact that later can reduce turnover rates [6]. Additionally, a complete evaluation should include static and dynamic elements [7]. [PERSON] proposes that attaining and maintaining high performance standards emerges as a predominant concern across various types of organizations, including private, public, for-profit, and non-profit entities. According to him, achieving high performance levels, accompanied by positive indicators, enhances the stability of the organization, while ensuring high levels of profitability, quality, productivity, motivation, innovation, and efficiency. Conversely, he asserts that low performance levels entail negative and dysfunctional outcomes for the organization. He argues that instances of low performance indicators are associated with specific circumstances [8]. The most classical sources of performance data generally face limitations in correctly capturing the dynamic nature of performance. Particularly, it is well known that supervisors or managers are the ones who carry out the performance appraisal, with colleagues, subordinates, or even customers providing additional feedback or notes to them [8]. Considering the dynamic nature of organizations, these sources and their ratings are often prone to rapid changes before the moment of the appraisal; therefore, these become unsuitable. Consequently, it also becomes more essential to adopt new methods for analyzing data from discrete sources and to gain a more comprehensive picture of employee performance in more organizational contexts [9]. Recent research applying data mining techniques to predict employee performance scores concludes that the most important goal is to minimize the influence of subjective factors and reduce personal biases as well [10]. Diverse performance attributes can direct the selection of appropriate data mining methods, promoting synchronization across multiple areas like business operations, technology, or information science. As a consequence, the process of performance evaluation tends to become a more scientific approach, lowering the arbitrary nature of artificial scoring. This development contributes to enhanced fairness, authority in assessments, and integrity, as well as simultaneously elevating employee engagement, productivity, and team collaboration [10]. The development of data mining technology is clear and well-known, considering the years spent undertaking research and investigating practical applications, a fact that has provided increasingly complex model types and even mining functions. This maturity enables the provision of decision support in employee performance evaluations by correctly choosing the right index systems and training models [11]. Recent efforts regarding employee performance classification have considered the implementation of various machine learning algorithms. Some studies targeted the exploration of psychological, socioeconomic and creative factors on employee performance and motivation [2; 12]. One important research study considered the use of prediction model construction algorithms, such as random forest, logistic regression, support vector machine, artificial neural network or naive Bayes [13]. Other studies underline the critical importance of employee performance in organizational operations, highlighting it as the central factor in determining survival and competitiveness. Additionally, employee performance significantly influences the rewards system within an organization.