student-retention

A machine learning project to predict student dropout risks based on demographic, academic, and socio-economic factors. Includes data preprocessing, feature engineering, model training, and deployment scripts. Designed to help educational institutions identify at-risk students and improve retention rates.

Data and Code to Accompany "Investigating and Communicating Library Instruction’s Relationship to Student Retention: A Study of Two Community Colleges"

Capacity-constrained early warning for student withdrawal and failure, from registrar data alone. Evaluated on future cohorts, scored by precision at adviser capacity and lead time.

Machine learning system achieving 87% accuracy in predicting student dropout risk using Python and scikit-learn

End-to-end data science project combining supervised classification (Random Forest), cohort clustering, and local RAG via Ollama to predict and mitigate student dropout risks.

Dropout early warning that arrives while you can still do something. Reverie Hacks 2026 Datathon.

Programming Sequence Improvement Program (PSIP): improving retention and student success in undergraduate CS programming courses at Arkansas Tech University. Published at IEEE HCIRA 2023.

AI-powered Student Dropout Prediction Platform using Machine Learning, FastAPI, and Streamlit.

Logistic regression analysis of first-year student retention and feature importance

Student Retention Intelligence System

Professional newsletter template for enhancing student retention and engagement within the Education industry.... | View more at LaravelMail.com

Machine-learning classification project that compares Logistic Regression, Random Forest, and XGBoost for identifying students at risk of withdrawing. The project emphasizes recall, interpretability, fairness evaluation, and responsible institutional use.

Top Student Success Platform (Opensource) 🌟 Star if you like it! 🌟

Data-driven student retention and welfare analytics platform that transforms messy student data into actionable insights.

Retention analysis of 600 first-year students. Tableau dashboards, logistic regression, and neural network modeling, with a selection-bias check on a voluntary first-year seminar.