🎓 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 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.

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

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.

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

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

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.

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

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.

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.

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.

This project aims to develop a Student Grade and Academic Performance Tracking System. For both teachers and students, keeping track of and controlling academic performance in the fast-paced world of education is essential. Grading can be done faster, student development can be tracked.

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.

Student performance in exams prediction and analysis using python

Predict exam scores using machine learning and behavioral data.

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.

教育データ（Kaggle）を用いて学習成果を分析し、LightGBM＋SHAPで指導施策に繋がる示唆を抽出