Student Dropout Prediction Project
Introduction
This project addresses the critical challenge of student dropouts in education, leveraging machine learning to predict at-risk students.
Objective
- To use data analysis and predictive modeling for improving student retention rates.
- Analyze various factors like academic performance, socio-demographic details, and student behavior to understand dropout dynamics.
Methodology
- Data Handling: Collection, preprocessing, and exploratory analysis of a comprehensive dataset.
- Key Features: Academic records, attendance, internet accessibility, social activity levels.
- Machine Learning Models: RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier , GridSearchCV , RandomizedSearchcv
- Techniques Used: SMOTE for balancing dataset, feature selection for impactful prediction.
Results
- Insightful understanding of factors influencing student dropouts.
- High accuracy in early identification of at-risk students using machine learning models.
Conclusion
This study highlights the efficacy of data-driven approaches and machine learning in tackling student dropout rates, offering valuable insights for educational institutions to enhance student retention and success.