Browsing by Author "Atinaf, Eristie"

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    Predict the Major Factors that Helps to Predict Employee Turnover in Government Organization Using Machine Learning:- the Case of Ethiopian Federal Court
    (Addis Ababa University, 2020-05-12) Atinaf, Eristie; Mulugeta, Wondwossen (PhD)
    Nowadays, Employee turnover is a serious issue in organizations. It affects the time, productivity, and stability of the given organizations. Employees are very important that helps the organization get success and gain revenue. So, Organizations need to know the key issues that the reason for employee turnover. Prediction models are highly associated with human resource management to identify the employee turnover patterns from employee previously recorded data. The objective of this research is to design a model and predicting staff turnover using a machine learning approach in the Ethiopian Federal court organization. For prediction three classification models namely, random forest, logistic regression and gradient boosting tree were used. The total datasets from the three federal court organizations were 3610 both active and terminated.For evaluate the prediction classification models the researcher was use confusion matrix, recall, precision and roc-curve to measure the performance of the classifiers. After evaluation, from the three classification models the finding shows that the best classification model is gradient boosting tree with an accuracy of 87.5%. Additionally, from the study it is found that the factors responsible for employee turnover are:-experience, salary, age and employee’s number of year service are the most significant factors. The factors martial and gender were low predictor variables on employee turnover in the federal court organization.The study concludes that the most reliable and accurate classification model to predict employee turnover isan ensemble – based learning technique gradient boosting tree that was found as the most suitable classifier for building the predictive model.