Abstract

Many employers are currently facing the costly challenge of employee attrition. This project study aims to present a prediction model that predicts employees’ erosion. To ensure project completion, the author scrutinized the Dubai Government’s employee data, which we obtained from its Human Resources Department. The employee data helped me determine the statistically essential factors that are associated with an employee’s decision to quit and understand the types of occupations where this model can be applied. The data gathered included information about employees’ specific demographics, seniority, income, and satisfaction. The dataset consists of 35 variables, with an Attrition category that entails 26 numerical variables and eight categorical variables as well as the data set’s target label, which is “attrition.” The prediction model was developed using the Python programming language with the aid of the Rapid Application Development (RAD) Methodology. The RAD Methodology is used as it allows for adjustments and efficient coding, thus reducing the time necessary for project development. The purpose of the study is to analyze the data obtained from the HR Department of the Dubai Government and outline the tendencies and factors that drive employees to decide on quitting Government employment. It could help government employers by providing in-depth knowledge as to why some of their employees choose to leave. The ability of employers to predict employee attrition before it happens will enable them to develop a strategy that would increase satisfaction and motivate employees to stay in their jobs.

Publication Date

5-2020

Document Type

Master's Project

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Ioannis Karamitsos

Campus

RIT Dubai

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