🎓 Student Performance AI

AI-powered web application that predicts a student's final academic score based on study habits, attendance, previous performance, assignment completion, and sleep hours.

🌐 Live Demo

👉 Try the Student Performance AI

🚀 Features

• 🎯 Predicts final student score
• 🏆 Classifies performance level
• ⚠️ Identifies academic risk level
• 💡 Provides personalized recommendations
• 🤖 Uses a Random Forest machine learning model
• 🌐 Interactive Streamlit web interface

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Streamlit
• Plotly
• Git & GitHub

📊 Machine Learning Model

The application uses a Random Forest Regressor to predict the student's final score.

Input Features

• Study Hours
• Attendance
• Previous Score
• Assignments Completed
• Sleep Hours

Model Evaluation

• Mean Absolute Error (MAE): 1.43
• R² Score: 0.94

Note: This project uses a small educational dataset and is intended as a demonstration/portfolio project.

📁 Project Structure

student-performance-ai/
├── data/
│   └── student_data.csv
├── models/
│   └── student_performance_model.pkl
├── app.py
├── train_model.py
├── requirements.txt
├── README.md
└── .gitignore