Implement Software Solutions to Reduce Student Dropout Rates at Various Educational Stages

Abstract

This project presents an Implement software solutions to reduces student drop rates at various eductional stages. Student dropout poses a major challenge for educational institutions, negatively impacting academic performance, graduation rates, and overall institutional development. Identifying students at risk of dropping out early allows for timely support and helps boost retention rates. This paper introduces an intelligent software solution that leverages machine learning to predict the likelihood of students dropping out. The proposed system is built with Python and the Flask framework, featuring a web interface crafted using HTML, CSS, and Bootstrap to ensure user-friendly operation. Historical student dataincluding academic, demographic, and financial characteristicsis preprocessed and then used to train classification models such as Decision Tree, Logistic Regression, and Random Forest. The system assesses the performance of these algorithms and picks the model that performs best in predicting whether a student is likely to graduate, stay enrolled, or drop out. The prediction is shown alongside a confidence score and relevant recommendations, helping educational institutions offer academic counseling, mentoring, and financial aid to students flagged as being at risk.