Student-Grade-Prediction/DEPLOYMENT_INFO.txt at main · Pacifique5/Student-Grade-Prediction · GitHub

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PROJECT NAME:
Student Performance Prediction - AI-Powered Academic Forecasting

SHORT DESCRIPTION (160 chars):
AI-powered web app predicting student performance using ML. Analyzes study habits, sleep, scores. Features dual AI models with smart validation.

DESCRIPTION (500 chars):
An intelligent web application that predicts student academic performance using machine learning. Analyzes study habits, sleep patterns, previous scores, extracurricular activities, and practice tests to forecast performance. Features dual AI models: one for performance prediction and another for validating input realism to detect impossible human behavior patterns. Built with Django REST Framework and scikit-learn, featuring a modern responsive frontend with real-time feedback and detailed insights.

WEBSITE:
http://127.0.0.1:8000/ (Local)
https://your-app-name.onrender.com/ (Production)

TOPICS (space-separated):
machine-learning artificial-intelligence student-performance education-technology predictive-analytics django python scikit-learn random-forest academic-prediction study-habits performance-forecasting rest-api data-science educational-ai behavioral-analysis input-validation ml-models frontend-backend student-analytics academic-success learning-analytics education-data-science performance-metrics study-optimization sleep-analysis extracurricular-impact django-rest-framework responsive-design ai-validation human-behavior-detection

KEYWORDS (comma-separated):
machine learning, artificial intelligence, student performance, education technology, predictive analytics, Django, Python, scikit-learn, random forest, academic prediction, study habits, performance forecasting, REST API, data science, educational AI, behavioral analysis, input validation, ML models, student analytics, academic success

CATEGORIES:
- Education
- Machine Learning
- Web Application
- Data Science
- Artificial Intelligence

TECH STACK:
Django 6.0.1, Django REST Framework, Python 3.13, scikit-learn 1.8.0, pandas, PostgreSQL, HTML5, CSS3, JavaScript

FEATURES:
✓ Dual AI Models (Performance + Validation)
✓ Smart Input Validation
✓ Real-time Predictions
✓ Human Behavior Detection
✓ Confidence Scoring
✓ Modern Responsive UI
✓ RESTful API
✓ Feature Engineering (15+ features)

USE CASES:
Students, Educators, Parents, Researchers, Educational Institutions

LICENSE:
MIT