student-performance

An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.

Professional Data Science project analyzing student performance factors using XGBoost, SHAP implementation, and K-Means Clustering for student segmentation.

A student grading system using Python that allows, entering the grades of a student, removing a student from the system, calculating the average grades of students and more!

SmartGrade AI is a predictive academic analysis system using machine learning and databases to monitor and improve student performance.

This project predicts the performance of a student in academics taking more than 10 factors into consideration.

Student Performance Predictor using Machine Learning. Predicts student marks based on study hours and attendance using Python, Pandas, and Scikit-Learn.

Built a pipeline using stats + SHAP to detect grading bias and evaluate teacher impact via attendance and marks data. Identified sensitive attribute influence (e.g., gender/religion) on student performance using explainable AI.

A machine learning system for predicting student performance based on workload and study patterns

Predicting student notes using Multiple Linear Regression, featuring both a custom Gradient Descent implementation and Scikit-learn.

🎓 Student Performance Prediction System using Machine Learning & Streamlit to forecast next semester CGPA with interactive insights and real-time predictions.

Machine learning experiments on student performance prediction. Inspired by tibeb (wisdom) in Amharic, this project explores regression models to understand how study factors influence exam scores.

Predicting student academic success using machine learning. Includes data preprocessing, model comparison (Random Forest, KNN), and feature importance analysis with 89% accuracy.

This Python program prompts users for four exam scores, sorts these scores, calculates the average excluding the lowest score, and assigns a letter grade based on the adjusted average. It is designed to help students visualize their performance across multiple exams, highlighting their highest, lowest, and average scores.

AI-based student performance analytics system using Django & Scikit-learn. Predicts grades and dropout risks using Logistic Regression and Decision Trees.

Python-based learning analytics platform for student performance benchmarking, trend analysis, question-level diagnostics, weakness detection, and time-efficiency analysis.

A supervised machine learning project that predicts student academic performance using Linear Regression with an interactive Streamlit dashboard.

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.

Student performance analysis using clustering, feature engineering, and ML-based academic success modeling.

Interactive Machine Learning web app that predicts student marks from study hours using Linear Regression, with real-time training, evaluation metrics, and visualization.

PyTorch and TensorFlow implementations of LSTM, Transformers, and autoencoders for modeling sequential student behavior.