The primary objective of this project is to develop a predictive model that can forecast the performance of students in their academic projects. The model aims to help educators and institutions identify students who may need additional support or intervention early in the project development process, ultimately enhancing overall student success.

Students’ Performance Prediction Using Machine Learning Approach

A machine learning web application built with Flask that predicts student performance based on input data. This project showcases practical skills in data preprocessing, model training, evaluation, and deploying ML models using Flask for real-time predictions.

An intelligent machine learning application that predicts student exam scores and provides personalized recommendations for academic improvement using advanced AI and data analytics.

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

Using Machine learning to predict a student final grade

GradeGuru is ML-powered grade predictor to forecast student exam performance. Input student information and get accurate grade predictions for proactive academic planning. Enhance student success and academic outcomes with GradeGuru.

An end-to-end automated machine learning pipeline for predicting student academic performance, covering data ingestion, preprocessing, model training, evaluation, and deployment-ready artifacts.

This software helps you to predict any student's performance status 🧑‍🎓🏫 on the bases of some key characteristics such as their Previous Marks🖊️, Attendance📅, Wasted time⏰, Extracurricular📖, etc. This project is made on React FrontEnd while BackEnd is powered on Fast API and Pandas 💻.

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

Working on a simple linear regression model for predicting students' exam scores. Using a dataset with features like study time, family size, and parental backgrounds. This work is for personal training and educational purposes.

Dead Simple Result Analysis for VTU Engineering Students

This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.

Student performance prediction using Python, Pandas, Matplotlib, and Scikit-learn.

Supplementary materials and LLM prompt templates for the paper: "Early Student Performance Prediction Using an LLM as a Pedagogical Co-Pilot and Few-Shot Classifier".

This repo contains my solutions for all tasks of elevvo internship tasks

ML-powered student performance prediction system with end-to-end pipeline. Predicts math scores using demographic & academic features through automated model selection (Random Forest, XGBoost, CatBoost) and Flask web deployment.

No description provided.

An interactive Streamlit app for predicting student performance using Random Forest regression and data visualization tools.

A machine learning project aimed at predicting student performance using various ML algorithms. Features data preprocessing, model training, and evaluation. Ideal for educational data analysis and academic research.

This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course

This project predicts students' math scores using machine learning and Flask for real-time predictions.

This project predicts students’ math performance based on demographic and academic attributes such as gender, parental education, lunch type, test preparation, and reading/writing scores.

This project builds a predictive model to estimate student exam performance using demographic and academic data. It applies machine learning techniques to analyze patterns and generate meaningful predictions.

AWS CICD Project

Pipeline de Deep Learning pour prédire la réussite étudiante. Implémente des réseaux de neurones (PyTorch, TensorFlow) pour la classification et la régression multi-targets, avec interprétabilité SHAP et une application Streamlit de production.

The Student Performance Indicator project aims to predict student academic performance based on various demographic and educational factors such as gender, parental education level, lunch type, and test preparation course.

A leakage-aware and uncertainty-aware framework for student performance prediction using learning behavior features, combining ensemble learning, conformal prediction, subgroup reliability, and interpretability for early-warning educational systems.

AI-powered student performance prediction system built with Flask and Scikit-learn featuring dynamic model training, role-based dashboards, analytics management, and interactive academic forum.

A machine learning approach to predict the student performance using their online activity and scores.