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.

This project predicts students' exam scores based on attendance, study hours, and past results using **Machine Learning** techniques like **Linear Regression** & **Decision Trees**. It also provides a Flask-based web dashboard where the admin can upload student data, view predictions, identify high-risk students, and export results.

This is an ML project, which is used to predict the student performance in the upcoming Examination based on various parameters about the student and their performance in the previous few exams. It also involves a Flask Rest APIs to facilitate prediction using the trained model.

Student Performance Prediction – End-to-End ML Pipeline 📌 Project Overview This project is an end-to-end machine learning pipeline that predicts student performance based on demographic and academic features. It covers the entire ML workflow — from data ingestion to deployment — and is packaged as a Flask web application for real-time predictions

🎓 Student Performance Predictor: An end-to-end ML project using Python, Scikit-Learn, and Flask. Features a modular pipeline, a 1,100-student dataset, and a clean web interface for real-time predictions.

Built an end-to-end ML regression project for student performance prediction with modular training and inference pipelines, Flask model serving, Dockerized runtime, and multi-cloud CI/CD workflows across AWS (ECR/Elastic Beanstalk) and Azure App Service.

This project develops a complete data science pipeline using Flask. It includes data preprocessing, model training, and deployment of an ML model that predicts student performance based on study hours and attendance, showcasing the workflow from data to web-based prediction.