# Data Analytics for Optimizing and Predicting Employee Performance
## 2 Background and Related Work
[PERSON] acknowledges that performance is linked to actions related to productivity, innovation, flexibility, production levels, commitment, absenteeism rates, and the overall image of the organization. He categorizes performance into high, moderate, and low levels, emphasizing that low performance is the least desirable state for any organization. He advocates for high performance levels, which correlate with increased productivity, innovation, quality, efficiency, and commitment, recognizing the potential for better prospects for the organization [14]. [PERSON] et al. introduced a method based on artificial intelligence for predicting the employee turnover while using a dataset that was built from state enterprises. Feature extraction was undertaken as well, to determine crucial factors affecting employee performance. For classification purposes, algorithms such as random forest, support vector machine (SVM), and linear regression (LR) were used again, together with AdaBoost, also concluding that there was a direct correlation between the employees' skills and associated performance scores [15]. Another important work focused on correctly and efficiently classifying employee job performance, based on DISC personality ((D)ominance, (i)nfluence, (S)teadiness and (C)onscientiousness). The classification for this personality test is built by comparing an individual personality with the standard personality test that the person took. DISC represents one of the most popular tests in this domain, standing for dominance, influence, steadiness and compliance. They built some models that were tested on a self-made dataset concerning the results of the DISC personality test for 2137 employees. For these models, the authors again used algorithms such as SVM, K-nearest neighbors (KNN), random forest (RF), LR, decision trees or naive Bayes. Regarding the results, it was concluded that, for the selection chosen, decision trees provided the best performance, with the lowest Hamming loss and the highest accuracy. In addition, as feature selection techniques, the results were better when using multi-label classification with a stacking technique [16]. [PERSON] et al. also investigated employee performance predictions using data mining, looking closely into the use of naive Bayes for a dataset which was based on 310 employees [17]. [PERSON] et al. focused on an approach based on the eXtreme Gradient Boosting (XGBoost) classifier, again using a self-made dataset with 73,115 labeled data registries. The introduction of feature extraction can also be identified in this last research study, while underlining the impact of turnover [18]. [PERSON] et al. looked closer into machine learning approaches, like K-nearest neighbors, SVM, naive Bayes, logistic regression, or random forest, regarding leaving the company. Their analysis included objective factors affecting worker wishes about turnover, considering both the correlation matrix for those features and running statistical analysis [19]. [PERSON] et al. explored various classification methods such as decision trees (DT), neural networks (NN), and K-nearest neighbors (KNN) for predicting talent outcomes. Their research aimed to identify the most accurate technique for processing Human Resource (HR) data. The findings indicated that the decision tree method was notably effective for talent forecasting within human resource management (HRM), demonstrating the highest level of accuracy. The data utilized in this research were gathered from an academic institution's staff database [20]. [PERSON] et al. proposed logistic regression, random forest, gradient boosting tree and extreme gradient boosting tree classifiers to identify employees' likelihood of turnover, while [PERSON] et al. suggested logistic regression and XGBoost, with the latter being the better algorithm for the same purpose, outperforming logistic regression [21]. Last, but not least, [PERSON] et al. introduced machine learning classifiers such as XGBoost and gradient boosting, while working with a real-time dataset [22].