🎓 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.

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.

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.

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

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

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.

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.

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.

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

Student Performance Dashboard with Next JS

This project analyzes student performance in the INSY107 course (2020–2023) using Python to uncover trends by gender, department, and year for academic improvement.

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.

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Machine Learning model to predict student performance using real-world data.

A machine learning project to predict student exam performance based on academic, social, and personal features. Built with Python and scikit-learn.

analyse student exam performance using nonparametric statistical methods

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.