Student Dropout Prediction

Keywords: Student, dropout

Background

The project focuses on utilizing deep learning techniques to predict student dropout rates. By analyzing academic and demographic data, it aims to create a predictive model that can identify students at risk of dropping out. This proactive approach could help educational institutions implement targeted interventions, improving student retention and success rates. The project contributes to enhancing education strategies by harnessing the power of DL methods to address a critical challenge in the education sector.

Aim & Objectives

Aim
To Predict the Student DropOut Data Using DL Techniques
Objective
Utilize DL techniques to forecast student attrition.
Train the model with academic information for precise classification.
Verify model effectiveness in anticipating dropout via DL approaches.

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. —