🧠 AI Cognitive Burnout Prediction System

📌 Project Overview

This project is an AI-powered system designed to predict student burnout risk and academic performance using machine learning models.

The goal is to identify early signs of burnout and provide actionable insights that can help improve student well-being and performance.

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🚀 Live Demo

👉 https://ai-burnout-prediction-system-d5mndrdnitgpufzx9muc5y.streamlit.app

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🎯 Problem Statement

Student burnout is a growing issue that negatively impacts academic performance and mental well-being.

This project aims to:

• Predict burnout levels (Low / Medium / High)
• Estimate academic performance
• Provide personalized recommendations based on user input

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🛠 Technologies Used

• Python
• Scikit-Learn
• Streamlit
• Plotly
• gTTS (Text-to-Speech)
• NumPy & Pandas

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🤖 Models Used

• Logistic Regression → Burnout Classification
• Random Forest Regressor → Performance Prediction

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📊 Key Features

• Burnout Risk Classification (Low / Medium / High)
• Academic Performance Prediction
• Interactive Visualizations (Radar Chart, Risk Gauge)
• AI-generated Voice Summary
• Personalized Recommendations

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💡 Key Insights / Value

• Lifestyle factors (sleep, workload, stress) significantly impact burnout levels
• Early detection of burnout can help improve academic outcomes
• Personalized feedback can guide students toward healthier habits

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🌐 Deployment

Deployed using Streamlit Cloud for real-time user interaction.

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📂 Project Structure

• Dataset (CSV)
• Model files (.pkl)
• Streamlit app (app.py)
• Jupyter notebook (model development)

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👤 Author

Kesar Deaulkar