🎓 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.

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 semantic approach towards student performance prediction using data mining techniques.

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.

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.

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

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

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.

Student performance in exams prediction and analysis using python

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

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.

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.

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.

ML project to predict pass/fail of students using logistic regression

This repository contains the code lines and raw data of my Marketing-based capstone research: Analysis of student satisfaction and the progress in their performance at an IELTS center in Vietnam. I collected real data from McIELTS center in Ho Chi Minh City.

A Python CLI tool for analyzing student performance using Pandas. Modular, beginner-friendly, and command-line operated.

A practical comparison of gradient descent algorithms to predict student performance using study and lifestyle data, with visual analysis.

SQL-based student performance tracking system using the OULAD dataset. Includes data cleaning, ER diagram creation, and educational analytics queries for portfolio demonstration.

machine learning web app that predicts students’ math exam scores using demographic and academic factors. Built with Flask, HTML/CSS, and a Random Forest model trained on the Student Performance dataset. Interactive, insightful, and easy to use.