lemayian23/academic-performance-ml

Machine learning web app predicting student performance at The Technical University of Kenya # The Technical University of Kenya Student Performance Predictor A machine learning web application built in Rust that predicts student academic performance based on attendance patterns. ## 🎯 Project Overview This project uses logistic regression to predict whether a student will pass or fail based on: - **Study hours** per week - **Attendance percentage** in classes ## 🚀 Features - **Machine Learning Model**: Logistic regression using Linfa crate - **REST API**: Actix-web backend with JSON endpoints - **Web Interface**: Beautiful HTML frontend for easy predictions - **Real-time Predictions**: Instant pass/fail predictions with confidence scores ## 📊 Model Performance - Trained on student academic data - Real-time prediction API - Accuracy metrics and model information endpoints ## 📈 Recent Updates - **Student Success Tips API** - Get evidence-based study recommendations - **Performance Analytics Dashboard** - View TUK student performance insights - **Enhanced Web Interface** - Better user experience for predictions ## 🛠️ Tech Stack - **Backend**: Rust, Actix-web - **ML**: Linfa, Linfa-logistic, NDArray - **Data**: CSV processing - **Frontend**: HTML, CSS, JavaScript ## 🏃‍♂️ Quick Start ```bash # Clone repository git clone cd student-performance-predictor # Run the application cargo run 📁 Project Structure src/ ├── main.rs # Main application logic data/ ├── students.csv # Training dataset Cargo.toml # Dependencies README.md # This file 📝 License MIT License - The Technical University of Kenya ## **3. Initial Git Commands & Commit Message** ```bash # Initialize git repository git init # Add all files git add . # Initial commit with meaningful message git commit -m "feat: Initial commit - TUK Student Performance Predictor - Implement logistic regression ML model for pass/fail prediction - Add Actix-web REST API with JSON endpoints - Create web interface for easy student performance checking - Include model trainin …