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.

🎓 A full-stack MERN + Machine Learning app where teachers can manage student records and predict academic outcomes (Pass/Fail) using a trained ML model based on attendance, study habits, and performance.

A semantic approach towards student performance prediction using data mining techniques.

A Machine Learning-powered platform to predict student performance and provide actionable insights for teachers. Built with Django, React, and Scikit-learn.

📊 Student Grade Analysis — Data mining project predicting academic performance using Python (Pandas, Scikit-Learn, SHAP). Includes visualizations, PDF report, and interpretable ML insights.

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!

Smart Student Performance Prediction App using ML and Django A web platform that predicts student outcomes using academic and behavioral data. It features data cleaning, EDA, feature engineering, and a Random Forest model. Includes dashboards for students, teachers, and admins with personalized stats, alerts, and PDF reports.

An end-to-end MLOps project demonstrating a modular machine learning pipeline for predicting student performance, featuring a Flask web interface and deployment on AWS.

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

Analyze and Predict student performance using ( SPAPS )

No description provided.

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.

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.

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

Engineered the Naive Bayes Algorithm from scratch. Utilized the student performance dataset from the UCI ML repo.

End-to-end student performance analysis using Python and SQL (SQLite), combining database querying with statistical exploration.

A complete Power BI Student Result Analysis Dashboard with toppers, KPIs, subject insights, mark ranges, and data modeling using Excel, Power Query & DAX.

🪐 8- Social Buss: Extension Project – Social Pulse
A machine-learning project analyzing anonymized student performance data through cleaning, exploration, feature engineering, and predictive modeling.

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

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.

Predict exam scores using machine learning and behavioral data.

Student performance in exams prediction and analysis using python

Predicting student GPA using lifestyle factors like study habits, sleep, and stress levels. A machine learning model built to help students and educators understand the impact of lifestyle choices on academic performance.

Thesis Paper & Documentation about Student Performance Prediction with Explainable AI & Fairness

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

Production-ready machine learning system to predict student final grades using structured data, sklearn pipelines, and FastAPI deployment.

This repository contains a machine learning model, JobMate Predictor, designed to predict the likelihood of a student's placement based on academic performance and other relevant factors.

📊 Analyze student performance with this interactive Power BI dashboard featuring KPIs, insights, and detailed documentation for easy replication.

Classification model to predict student performance in the Saber Pro exams in Colombia. This repository includes exploratory data analysis, data preprocessing, and machine learning models. Ideal for educational data scientists and researchers interested in academic performance prediction.

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