Student Burnout Prediction System 🎓

A beginner-friendly machine learning project to predict student burnout risk using daily lifestyle inputs.

🚀 Features

• Synthetic Data Generation: Custom script to create a balanced dataset.
• Machine Learning Model: Random Forest Classifier trained to categorize burnout risk.
• Interactive UI: Polished Streamlit application with sliders, prediction cards, and personalized advice.
• Visual Feedback: Color-coded risk levels (Green: Low, Yellow: Medium, Red: High).

🛠️ Project Structure

• data_generation.py: Script to generate student_burnout_data.csv.
• train_model.py: Script to train the model and save model.pkl and scaler.pkl.
• app.py: Main Streamlit application file.
• requirements.txt: Python dependencies.

🏃 How to Run

1. Install Dependencies

Open your terminal and run:

pip install -r requirements.txt

2. Generate Data and Train Model

Run these scripts in order to set up the model:

python data_generation.py
python train_model.py

3. Launch the App

Start the Streamlit dashboard:

streamlit run app.py

🧠 Model Features

The system takes the following inputs:

1. Study Hours: Mental load per day.
2. Sleep Hours: Recovery factor.
3. Screen Time: Digital fatigue factor.
4. Stress Level: Subjective emotional state.
5. Assignments: Weekly academic pressure.

⚖️ License

MIT