# Data Analytics for Optimizing and Predicting Employee Performance
## 3 Methods and Results
### Data Preparation
#### 3.3.4 Data Selection  
Considering the latest developments in technology, a huge number of computer and Internet applications have generated considerable amounts of data at an extraordinary speed. This kind of data has an important factor in common: high dimensionality, which generally imposes a large challenge for the analysis of data. Therefore, feature selection has been proven to be very effective in both theory and practice, when considering the subject of high-dimensional data analysis and magnifying learning productivity [27]. We can see a detailed process flow of how this feature selection method generally works in Figure 7, as follows:  
Feature selection is the process of procuring a subgroup from the original dataset, according to certain ways of selecting the considered variables. This contributes enormously to the process of compressing data cleaning, wherein the redundant and unrelated features are removed. Feature selection methods can help to pre-process the learning algorithms, while adequate feature selection outcomes can improve the accuracy of the learning process, reduce the learning time, and simplify the learning results [28]. Therefore, we can consider feature extraction, together with feature selection, as two important means of reducing dimensionality. In contrast with feature selection, feature extraction methods need, generally, to transform the original data into different features, which are characterized by a strong pattern identification ability, in which the original data can be seen as attributes without firm recognition abilities. Figure 7: Feature selection process (source: [[https://www.researchgate.net/figure/A-General-Framework-of-Wrapper-Feature-Selection-Methods_fig8_301856754](https://www.researchgate.net/figure/A-General-Framework-of-Wrapper-Feature-Selection-Methods_fig8_301856754)]([https://www.researchgate.net/figure/A-General-Framework-of-Wrapper-Feature-Selection-Methods_fig8_301856754](https://www.researchgate.net/figure/A-General-Framework-of-Wrapper-Feature-Selection-Methods_fig8_301856754)), accessed on 9 April 2024). Feature selection has been demonstrated to increase the performance of classification across multiple applications. In recent studies, it has been used for a high-dimensional cancer microarray dataset, in the field of Arabic text classification, and in predicting heart diseases using bio-inspired optimization techniques [29; 30]. The utilization of feature selection is important, especially in the areas where decision-makers do not have the ability to decide the relevance of each feature for the specific domain. The literature in the area of feature selection can be categorized into three major areas, organized considering the selection process: filter, wrapper, or embedded methods. Of all these methods, filter techniques have attracted the most attention because of their computational efficiency. Moreover, these are independent of classifiers, therefore making these techniques suitable for problems involving a high number of features [31; 32]. To look even closer into the existing types of feature selection methods, below there are some of the examples for each category exemplified:  
1. Filter methods such as univariate feature selection, correlation-based feature selection, and variance thresholding. 2. Wrapper methods: Wrapper methods are a type of feature selection technique that involves training a model iteratively with different subsets of features and evaluating their impact on the model's performance. One of the most popular techniques for this is recursive feature elimination (RFE). 3. Embedded methods: A type of feature selection technique that includes feature selection as a vital aspect of the model training process. One example here is related to variable importance feature selection (from tree-based models) [33]. In order to apply, to our particular research, the optimal methods for feature selection and to observe, as well, how they behave, we have chosen some of the most popular ones from each of the categories mentioned above.