## 2 Materials and Methods  
This research follows a structured and reproducible approach to ensure the validity and replicability of results. The methodology consists of multiple stages, each employing specialised computational tools and machine learning techniques for data integration, preprocessing, analysis, and modelling. The data collection and integration process was carried out and is detailed below. The first step is data collection, which is critical for determining the success of a predictive model [29]. Human resources data is often dispersed across multiple systems, making it necessary to standardise and integrate diverse data sources into a unified structure to ensure consistency and comparability. The primary datasets for this study were sourced from the University CEU Cardenal Herrera, following compliance with the General Data Protection Regulation (GDPR). The dataset spans a historical record of five academic years (2018-2022) and includes information on university academic staff. The data sources are as follows:  
* Workday (Workday, Inc. (Pleasanton, CA, USA)) and A3 (Wolters Kluwer (Alphen aan den Rijn, The Netherlands)): Administrative and compensation-related data. * Cornerstone (Cornerstone OnDemand Inc. (Santa Monica, CA, USA)): Employee training, performance, and talent management data. * Own University IT Systems: Organisational structure and faculty employment records. To ensure semantic coherence, integration required format transformation and standardisation, aligning different fields despite originating from separate platforms. The Extract, Transform, Load (ETL) methodology was applied to clean, transform, and load the data into a unified dataset, allowing for seamless analysis. The process of data preprocessing and feature engineering was conducted, as outlined below. Given the considerable number of records in the dataset, an initial exploratory analysis was conducted to:  
* Identify and handle outliers, redundancies, and null values. * Correct typographical errors and standardise variable formats. Following this, feature engineering was performed to enhance data quality. The final dataset included 1144 individuals and 103 features, of which 72 were quantitative. After filtering the dataset to focus on tenure-track faculty, adjunct professors were excluded, resulting in a refined dataset of 599 individuals. A key qualitative variable within the dataset is \"commitment,\" which indicates whether a faculty member was actively employed at the university as of 31 August 2023. Then, Data Normalisation and Correlation Analysis were performed. Since machine learning techniques based on distance measures require data normalisation, the 72 numerical features were standardised to a common scale without distorting their relative differences. To explore relationships between variables, a correlation matrix was computed using Kendall's correlation coefficient, which measures monotonic associations between quantitative variables. Based on the correlation structure, sub-datasets were constructed, preserving only significantly correlated variables. Moreover, dimensionality reduction using Principal Component Analysis (PCA) was used to reduce the dataset dimensionality while preserving key information. This method, introduced by [30] and further developed by [31], transforms correlated variables into uncorrelated principal components ranked by importance. The following considerations guided the PCA application:  
* Eigenvalues: Used to rank components in terms of information retention. Components with the highest eigenvalues were prioritised. * Eigenvectors: Define the direction of principal components, ensuring they remain orthogonal. * Projection Principles: The first principal component maximises data variance, while subsequent components are perpendicular, forming an orthogonal coordinate system. * Goodness-of-Fit Measure: The elbow method was applied to determine the optimal number of components by explaining at least 70% of the variance [32].