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Student Performance Predictor
This is a web application that predicts a student's performance (Pass/Fail) based on their attendance, study hours, and assignment scores. The application is built with a React frontend, Flask backend, and a machine learning model.
Duration: 2 months
Role: Full Stack Developer
Category: Full Stack
Solo Project
CompletedIntermediate
Project Overview
An intelligent web application that leverages machine learning to predict student academic performance. This project demonstrates the integration of data science with web development, creating a practical tool for educational insights and early intervention strategies.
The application uses supervised learning algorithms to analyze student data patterns and provide accurate performance predictions. The system features a clean, intuitive interface for data input and displays comprehensive prediction history for tracking accuracy over time.
The technical implementation showcases full-stack development with machine learning integration, demonstrating skills in both web development and data science domains.
Key Features
User-friendly interface for entering student data
Predicts whether a student will pass or fail
Displays prediction history
Styled for a modern look
Key Learnings
Machine learning model integration with Flask
Scikit-learn for predictive modeling
Full-stack development with Python backend
Data visualization and user experience design
Project Details
Type:Personal Project
Size:Small
Deployment:Custom Server
Target:B2C
Technologies Used
ReactFlaskPythonScikit-learnSQLiteMachine Learning
Technical Challenges
- • Integrating machine learning model with web application
- • Ensuring model accuracy and reliability
- • Creating intuitive data input interface
- • Managing prediction history and data persistence