# 🎓 Stress Level Predictor

🏆 Project Overview

Predict student stress levels (Low 🟢, Medium 🟡, High 🔴) using psychological, academic, and lifestyle survey data via machine learning. Empower universities to intervene early and support student well-being.

🔑 Key Features

• 🧹 Data Cleaning & Preprocessing
  
• 📊 Exploratory Data Analysis (EDA)
  
• 📉 Dimensionality Reduction with PCA
  
• 🤖 Model Training:
  
  • Decision Tree 🌳
    
  • Random Forest 🌲
    
  • Support Vector Machine ⚔️
    
  • K-Nearest Neighbors 📍
    
  • Logistic Regression ➗
    
  • Gradient Boosting 🚀
    
• 📈 Model Evaluation: Accuracy, Precision, Recall, F1-Score
  
• 💡 Comparison Dashboard of all models
  

📂 Repository Structure

STRESS-LEVEL-PREDICTOR/ ├── 📁 data/ # Raw & processed datasets │ └── Dataset.csv # 1,100 records × 21 features ├── 📁 docs/ # Markdown docs │ └── Project Report.docx # Final project document ├── 📁 notebooks/ # Exploratory Jupyter notebooks │ └── Code.ipynb # End-to-end pipeline ├── 📁 src/ # Python scripts │ ├── data_preprocessing.py # Load → Clean → Scale → Split │ ├── eda.py # Plots & insights │ ├── modeling.py # Train Decision Tree + metrics │ └── evaluation.py # Train & compare all models ├── 📄 .gitignore # Sensitive files to ignore ├── 📄 LICENSE # MIT License ├── 📄 README.md # This file └── 📄 requirements.txt # Python dependencies

💾 Installation

1. Clone
   bash git clone https://github.com/akamohid/STRESS-LEVEL-PREDICTOR.git cd STRESS-LEVEL-PREDICTOR

2. Setup environment
   bash python -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # Windows pip install -r requirements.txt

🚀 Quick Start

1. Preprocess Data
   bash python src/data_preprocessing.py
2. Run EDA
   bash python src/eda.py
3. Train & Evaluate Decision Tree
   bash python src/modeling.py
4. Compare All Models
   bash python src/evaluation.py
5. Or open the Jupyter Notebook for a unified, annotated workflow:
   bash jupyter lab notebooks/Code.ipynb

📊 Results Overview

| Model | Accuracy | Precision | Recall | F1 Score | |------------------------|:--------:|:---------:|:------:|:--------:| | Decision Tree 🌳 | 0.90 | 0.90 | 0.90 | 0.89 | | Random Forest 🌲 | 0.89 | 0.88 | 0.89 | 0.87 | | Gradient Boosting 🚀 | 0.88 | 0.87 | 0.88 | 0.86 | | SVM ⚔️ | 0.87 | 0.86 | 0.87 | 0.86 | | KNN 📍 | 0.85 | 0.85 | 0.85 | 0.84 | | Logistic Regression ➗ | 0.86 | 0.86 | 0.86 | 0.86 |

📌 Decision Tree delivered the best blend of performance and interpretability.

📚 Literature Review

See docs/literature_review.md for details on: - Filippis & Al Foysal (2024)
- Arya et al. (2024)
- Singh et al. (2024)
- …and others.

📈 Git Workflow

bash git init git remote remove origin 2>$null git remote add origin https://github.com/akamohid/STRESS-LEVEL-PREDICTOR.git git add . git commit -m "Initial commit: Full Stress Level Predictor pipeline" git branch -M main git push -u origin main

👥 Team Members

• Mohid Arshad — GitHub | LinkedIn
  
• Mohammad Umar — LinkedIn
  
• Mohammad Hasnain — LinkedIn
  
• Tahir Mehmood — LinkedIn

📄 License

This project is released under the MIT License. See LICENSE for details.

📬 Contact & Feedback

Made with ❤️ by Mohid Arshad.
✉️ Email: akamohid@gmail.com
🔗 LinkedIn
Feel free to open issues, contribute, or drop a ⭐!