🧠 Mental Health Prediction Using Machine Learning

This project aims to build a machine learning-based system to predict mental health conditions such as anxiety, depression, and panic attacks among students using Decision Tree and Random Forest algorithms. The goal is to enable early detection and help professionals take preventive actions in mental health care.

📌 Project Objectives

• Analyze student mental health data to identify key influencing factors.
• Build and compare machine learning models (Decision Tree, Random Forest).
• Evaluate model performance using metrics like accuracy, precision, recall, and F1-score.
• Visualize insights and model performance for clarity and decision-making.
• Provide actionable predictions for mental health status.

🧪 Technologies Used

• Programming Language: Python
• Platform: Google Colab
• Libraries:
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Scikit-learn

🛠️ Project Structure

MentalHealthPrediction/ │ ├── data/ # Dataset files (CSV) ├── notebooks/ # Jupyter/Colab notebooks ├── models/ # Trained ML models ├── screenshots/ # Model result charts & visualizations ├── mental_health_prediction.py # Main Python script (if applicable) ├── README.md # Project overview └── requirements.txt # Python dependencies

🔍 Methodology

1. Data Collection: Student mental health dataset sourced from public surveys.
2. Preprocessing: Handling missing values, encoding categorical variables, and feature selection.
3. Modeling:
   • Decision Tree Classifier
   • Random Forest Classifier
4. Evaluation:
   • Accuracy: DT (64.52%), RF (87.10%)
   • Confusion Matrix & Classification Report
   • Feature Importance visualization

📊 Key Results

• Random Forest outperformed Decision Tree with a higher accuracy and better generalization.
• Important features affecting predictions include gender, GPA, marital status, and course.

📈 Visual Outputs

• Anxiety, Depression, Panic Attacks – by Gender & Course
• Confusion matrices for both models
• Algorithm comparison bar chart

🔮 Future Enhancements

• Integrate additional ML models like SVM, XGBoost, or LSTM for better accuracy.
• Include behavioral, social media, and sleep data for richer insights.
• Develop a web interface to offer real-time prediction and mental health support suggestions.

📚 References

Refer to the DOCUMENT (MINI PROJECT).docx for a full list of academic papers and resources.

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Note: This project is intended for educational and research purposes only and not for clinical diagnosis.