Análisis y predicción de la deserción de empleados : un caso de estudio en la industria de software colombiana

Abstract

The objective of this study is to carry out the analysis and prediction of the desertion of employees of a software company in Medellín, based on a private database that contains 19 characteristics of 1497 workers, where 900 are active in the company and the rest have left their job. In the first place, a descriptive and exploratory analysis was carried out, where it was found that there was some variables that did not contribute information to the model, such as: Type of identification, start date of the contract, among others, also in this part the correlation of some variables and proceeded to eliminate them from the set of descriptive characteristics of the problem, since that leaving them would be leaving redundant information in the model. Second, they trained 4 machine learning models (Niave Bayes, Random Forest, Decision Tree, Logistic Regression) and the results obtained by each were compared, in order to find the one that best fits the problem of labor desertion, in this step it was found that the best classifier of machine learning is a decision tree (Decision Tree) with 14 layers, since metrics such as its curve of learning and ROC curve gave better results than the other two trained models.