# Data Analytics for Optimizing and Predicting Employee Performance
## 4 Discussion  
Considering the objective of this research, it is certain that there is a clear focus on developing a machine learning algorithm and providing a method to create a more automatic and objective model for scoring performance. However, it is important not to forget the fact that these kinds of approaches can be dangerous, as, in case something is not working as expected from the start of the process, humans would not be available to correct that [47]. Therefore, as the study has been conducted on a small dataset, this should only be used as a starting point. Humans can supervise or they can also develop similar analyses and the results could be compared, while very different results could be flagged for a second analysis. In this way, not only could this process be improved, but the subjectivity could be lowered. Moving on to the data that have been used, considering the standard dataset specially created for this kind of analysis, it is much simpler to address the accuracy and balance of the dataset, so that the data can be used at their real value, as opposed to other situations where this can represent a large challenge in the process [48]. Therefore, specifically to address this important step, the first section of the research focused on feature selection, not only as a method to optimize the classification algorithm, but also as a conclusion to understand the best variables for objectively determining performance scores, it was concluded that Salary, Position, and EmpSatisfaction are commonly seen, no matter the method used; however, it proved to us, once again, that compensation, along with work/life balance and additional perks, contribute enormously to overall performance scores. Moreover, variables such as DaysLateLast 30, RecruitmentSource, and Absences also contribute a lot to performance scores for most of the methods. In this area, other studies confirmed that JobTitle, UnivType, and Age can be decisive for employee performance. In addition, other educational factors, such as degree and grade, affected performance scores very little; however, these results were all obtained using classification techniques [49]. On the other hand, even though similar research studies have been presented, they do not underline or reconfirm the most significant attributes that will lead to the best performance in the end [13; 17]. Looking at related papers regarding types of classification problems using supervised algorithms, it was determined, once again, that classical algorithms are still the most popular ones used across different datasets and data types. In human resources management, the most classical ones have been chosen for tests, and results have varied. Some decided that naive Bayes behaves well in terms of some data types and business problems, while others concluded the same for decision tree and XGBoost. However, looking closer, XGBoost has been mostly tested on attrition problems and classification, rather than on determining employee performance scores. Therefore, in addition to the general algorithms used for previous classification problems in predicting performance scores, this research added artificial neural networks and XGBoost. While the former provided some of the worst results, XGBoost managed to provide the opposite. Therefore, the search could confirm that XGBoost could easily be the best solution for classification problems even when talking about predicting employee performance scores, with some of the best results in terms of time and resource consumption as well. Another important factor added to this study was observing how all these algorithms performed when hyperparameter tuning was added for each of them. Analyzing related works, it can be concluded that grid search was one of the most used and well-known methods for this type of optimization, because of its simple implementation [50]. However, while this is a classic approach, it comes with some downsides as well, especially regarding computational costs.