# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Technique Overview
#### 3.1.4 Step 4: Model Selection and Data Splitting  
The model selection and data separation process were critical components of this study, which aimed to ensure the robustness and reliability of the machine learning models used to predict employee performance. To identify the best-performing model, a diverse set of algorithms, including Linear Regression, Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), gradient boost machine (GBM), bounded decision trees, Support Vector Machine (SVM), and XGBoost, were evaluated. To achieve this, the dataset was first divided into three subsets: training, validation, and testing datasets. The training set comprising most of the data was used to fit the models and optimize their parameters. The validation set was employed to fine-tune hyperparameters and prevent overfitting, ensuring that the models generalized well to unseen data. Finally, the test set was used exclusively to evaluate the final performance of the selected models, providing an unbiased assessment of their predictive accuracy. The model selection process involved comparing algorithms across various performance metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), alongside computational metrics like CPU time and  
Figure 2: Task allocation and CF calculation. memory usage. This thorough approach enabled the identification of ANFIS as the most effective algorithm to model complex relationships in the dataset with superior accuracy and robustness. The structured data splitting methodology ensured that the evaluation results reflected the true predictive power of the models, laying a strong foundation for their application in real-world scenarios. The dataset used in this study consists of task assignments and employee profiles obtained from public sector organizations in the Region of Western Greece. The dataset used in our study consists of 638 employee records. Each record is characterized by four key factors: academic qualifications, professional experience in the public and private sectors, and age. These factors provide a detailed view of the employees' skills, roles, and demographics, enabling us to explore patterns and make meaningful predictions as seen in Table 1 below. In terms of academic qualifications, the dataset reflects a diverse range of skills. Over half of the employees hold at least one Bachelor's degree, with 53% having completed their first degree and some employees holding a second Bachelor's degree. Advanced qualifications are also well-represented, with 35% of employees holding a Master's degree, few of them holding an NSPA degree, and even fewer possessing a Ph.D. Participation in professional seminars is another common characteristic, with 47% of employees having attended at least one seminar. This diversity of qualifications highlights a workforce with significant educational accomplishments. Leadership roles in the public and private sectors are another focus of our dataset. In the public sector, 23% of employees are Heads of Small Departments, 12% are Heads of Departments, and 3% are General Managers. Regarding experience in the private sector, these roles are slightly more prevalent, with 26% served as Heads of Small Departments, 12% as Heads of Departments, and 5% as General Managers. These statistics demonstrate a workforce with considerable leadership experience, particularly in the private sector. The dataset also captures the age distribution of the employees, which is skewed toward older age groups. A significant portion of the workforce, 47%, is aged 50 years or older. Employees aged 40 to 49 years make up 30% of the dataset, while those aged 30 to 39 account for 16%. Only 5% of employees fall within the 20 to 29-year age range. This age distribution aligns with the dataset's emphasis on experienced professionals who likely possess advanced academic qualifications and leadership roles. The dataset is moderately balanced, ensuring robust analysis.