AI-powered academic guidance system that predicts student GPA and recommends personalized courses using collaborative filtering, Jaccard similarity, and PageRank algorithms. Built with Python ML backend, Node.js API, and web frontend for real-time academic planning assistance.

A machine learning-based educational technology system that predicts student academic outcomes through three specialized models: final exam mark prediction, dropout risk assessment, and pass/fail forecasting. Built with Python, Flask, and scikit-learn to help educational institutions identify at-risk students and implement timely interventions.

Predicting students' academic performance using machine learning in Python. Covers data preprocessing, EDA, and model training to help educators identify students who may need additional academic support.

ML system predicting student academic success using grade and performance data - combines classification models with uncertainty quantification to identify at-risk students with confidence scoring.

AI-powered student performance predictor using Logistic Regression (75.6% accuracy) analyzing 7 features including study hours, attendance, assignments, and participation to predict grade categories (Excellent/Good/Average/Poor) with 95% recall for identifying struggling students, providing personalized improvement insights.