student-dropout

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.

Prediksi risiko dropout mahasiswa & dashboard interaktif performa akademik.

A machine learning model predicted student dropout rates and academic success by identifying at-risk students and providing targeted interventions.

The classification problem of student dropout data of an institute

End-to-end ML system to predict student dropout risk. Gradient Boosting (88% acc, 96.6% AUC) + FastAPI backend + real-time web UI.

Kicking off my Monday coding routine with Week 27! This week, I moved into fundamental Machine Learning algorithms by building a **Logistic Regression** classifier entirely from scratch using NumPy.

Student dropout prediction using feature selection, model comparison, hyperparameter optimization, and ensemble learning on the UCI student dataset.

Machine learning and MLP comparison project for predicting student dropout, enrollment, and graduation using the Kaggle/UCI student success dataset.

End-to-end ML project for student dropout risk prediction using Python, Scikit-learn, XGBoost, LightGBM, SHAP and FastAPI.

Student Dropout Analysis using Excel, SQL, Python and Power BI to support SDG 4 (Quality Education).

Machine learning project using KNN to predict student dropout risk and academic performance based on educational data

Work on IEEE Journal Special Issue on Early Prediction and Supporting of Learning Performance

An AI-powered Machine Learning application that predicts student dropout risk using Random Forest and an interactive Streamlit dashboard.

Student dropout prediction using Python, data analysis, and machine learning techniques

Système Intelligent et Explicable de Prédiction Précoce du Décrochage Étudiant — PFE Licence d'Excellence IA, FSBM

Machine Learning web application to predict student dropout risk using a Random Forest Pipeline and Streamlit.

Student-dropout prediction with a fairness audit and a GenAI explanation layer. A mistake-driven ML capstone on the UCI Portalegre dataset.