Student Performance Prediction Web App Using Machine Learning

Overview

The Student Performance Prediction Web Application is a powerful tool built using Python, Flask, and TensorFlow to help students, educators, and institutions make data-driven decisions about academic progress. By analyzing input parameters like study hours, attendance, and past grades, this app provides accurate predictions of final scores using a trained neural network model.

Key Features

• Real-Time Predictions: Get instant academic performance forecasts based on user inputs.
• Interactive UI: Modern and responsive interface built with HTML, CSS, and JavaScript.
• Data Visualization: Visualize performance metrics using Plotly charts for better understanding.
• Printable Results: Generate and print detailed analysis reports for future reference.
• Performance Categorization: Classify predicted outcomes into categories such as "Excellent", "Good", "Average", or "Needs Improvement".
• Recommendation Engine: Get personalized suggestions for improving academic performance.

Applications

• Educational institutions can use it for early intervention strategies.
• Students can track their progress and identify areas needing attention.
• Teachers can assess student performance trends and offer targeted support.
• Researchers can explore how different factors affect academic success.

Technology Stack

This project is powered by:

• Backend: Flask (Python)
• Frontend: HTML5, CSS3, JavaScript
• Machine Learning: TensorFlow & Keras
• Visualization: Plotly.js
• Model Deployment: Local H5 file

Why Choose This Project?

This web application is not only an excellent example of machine learning in education but also a practical solution for improving academic outcomes. With easy installation, intuitive UI, and detailed analytics, it's perfect for both beginners and advanced developers looking to build impactful ML-based tools.