# Data Analytics for Optimizing and Predicting Employee Performance
## 3 Methods and Results
### Data Modeling
#### 3.4.2 Building the Models  
In order to continue with this process, the dataset spilt will first be considered, as it will help us to obtain a better distribution of data across the training and testing phases. The split will be 80% and 20% for the two categories. The next step is to use the previously mentioned algorithms to build each of the models accordingly. In the next section, each of them will be briefly described, considering that these were chosen for this kind of analysis [35]. * Random Forest Random forest is one of the most recurrent algorithms used in building predictive models. This algorithm works in a very simple manner, by dividing predictor variables into multiple binary slits in order to predict outcomes. Using a selected dataset as training data together with selected subsets of predictor variables that are randomly selected, this algorithm builds multiple classifications and regression trees. Considering the results of the individual trees, there is a prediction resulting from each observation. In conclusion, random forest offers a higher accuracy than any other decision tree model, maintaining at the same time the same level of benefits specific to tree models [36]. * Decision Tree Considered to be one of the most popular classification algorithms, the decision tree is a mathematical tool that helps in the decision-making process. This model shows the decision and also possible outcomes, with the help of a flowchart structure that uses nodes and leaves. Multiple nodes build a node network, while those that do not have any incoming edges are generally referred to as commonly. On the other hand, all the other nodes can have outgoing edges and are considered to be internal or tested nodes, or with incoming edges, and are considered to be decision nodes. In order to decide which node should be used, the decision tree algorithm will take into consideration the information received from each node. A root node will be considered a node with the highest gain, while all the other nodes will be set on using the rest of the nodes [36]. * Support Vector Machine (SVM) Support vector machine is part of the linear classifiers category of algorithms. This works by identifying different classes while separating samples using decision boundaries, commonly known as hyperplane. Linear, together with non-linear, data can be classified with a support vector machine. Nonetheless, it is largely known as the maximum margin classifier, considering the fact that it can maximize the geometric margin while minimizing the empirical classification error [36]. * K-Nearest Neighbors (KNN) In the current scenario, where there are many algorithms for the purpose of classification, KNN is considered to be one of the simplest. This method involves the action of grouping of unknown data points that have an already known class. Lastly, using the k-value along with the nearest neighbor number of the data points, the nearest neighbor is calculated [37]. * Artificial Neural Network (ANN) It is thought that artificial neural networks are simulations of our biological brain. They are composed of numerous simple processing units connected through weighted links. These processing units, often referred to as \"neurons\", only process information available locally, either stored within or received through the weighted connections. Each unit can receive inputs from and transmit outputs to many other units. Individually, a processing unit is not very potent, producing a scalar output--a simple nonlinear function of its inputs. However, the collective operation of many such units, when appropriately combined, unleashes the system's true capability [38]. * XGBoost  
While the previously described algorithms are some of the most classical ones used generally for multi-classification, XGBoost comes as an implementation of grading-boosting decision trees that builds predictive models, combining the predictions of multiple models in an iterative manner.