Employee Retention Prediction

Keywords: Employee, Retention

Background

Deep learning techniques have been developed to analyze or predict various types of problems in real-time scenarios. These techniques are most useful in different kinds of scenarios where the size of the datasets is large in number, and it helps in solving complex tasks in an easy manner. Prediction of Employee retention will help the company to analyze how to use the resources in improving the company’s financial status. There are several factors that contribute to the retention of an employee, and they include their performance over the years, number of projects completed, number of promotions, and range of salary. ANN builds an analysis of the entire dataset, and it will be able to help with the prediction of retention. ANN uses different kinds of layers in the process and there will be several backward and forward propagation steps to predict the results with good accuracy. Retention of the employee is a binary classification, it has only two options to predict the status of the retention. Finally, the author has used the ANN architecture to perform the predictions in the retention of employees and achieved better results with an accuracy of around 97%.

Aim & Objectives

Aim
To Predict the Employee Retention using DL Techniques
Objective

Develop an ANN model to predict employee retention, enhancing workforce management strategies and reducing turnover.
Create an ANN system that analyzes employee data and engagement factors to forecast retention probabilities.
Evaluate the ANN's predictive performance, aiming to provide actionable insights for improved employee retention and satisfaction.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —