Στατιστικά μοντέλα πρόβλεψης στη διαχείριση ανθρώπινου δυναμικού
Statistical predictive models in human resources management
View/
Keywords
HR ; HRM ; Διαχείριση ανθρώπινου δυναμικού ; Λογιστική παλινδρόμηση ; Logistic regression ; SVM ; Decision tree ; Random forest ; Στατιστικά μοντέλα πρόβλεψηςAbstract
Human Resource Management is critical to the success and competitiveness of companies. One of the key challenges in Human Resources Management is the retention and development of employees in any organization. In our days, the phenomenon of frequent job change has been observed. Employees of all ages are not afraid to work in new companies, with the ultimate goal of better remuneration and better working conditions. This phenomenon has many consequences for the companies. Ιn order to face this phenomenon, a Human Resources department needs tools. These data-based tools can help identify the reasons that lead employees to leave, in order to take measures and prevent them. In this thesis, through the available dataset, which includes personal and professional information about each employee, using logistic regression and machine learning algorithms, we will try to predict the employee's departure or retention in the company. The theoretical background of these methods, such as graphs and plots of the variables and descriptive measures, are also given. Finally, the results of the models are compared using appropriate metrics to evaluate their performance in order to reach the most appropriate and efficient model for our prediction.
Postgraduate Studies Programme
Εφαρμοσμένη ΣτατιστικήDepartment
Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής ΕπιστήμηςNumber of pages
96Language
GreekCollections
Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα