Mental Health Prediction Web App: AI & Machine Learning for Early Detection

🔍 Project Overview

Our Mental Health Prediction Web App is an AI-driven platform designed to enable early and accurate detection of potential mental health conditions.By leveraging multiple machine learning algorithms—including Logistic Regression, Random Forest, SVM, KNN, Naive Bayes, Decision Tree, and XGBoost—this solution transforms user data into actionable insights.It empowers individuals and professionals to take proactive steps toward mental well-being while showcasing the power of AI in mental health analytics.

🏆 Key Achievements

• Comprehensive ML Integration: Implemented and compared Logistic Regression, Random Forest, SVM, KNN, Naive Bayes, and Decision Tree for mental health prediction.
• Early Detection: Enabled timely identification of anxiety, depression, and burnout using behavioral data and pattern recognition.
• User-Centric Web App: Delivered personalized insights and mood tracking through a secure, intuitive Django-based platform.
• Open & Accessible: All Jupyter notebooks available on GitHub; full web app live at datatrendx.com.

🎥 Project Explanation Video

Video Highlights

• Live Mental Health Accuracy Detection demo
• Machine Learning architecture walkthrough
• Performance metrics and clinical insights

🛠️ Technical Stack

Frontend

• Django Web Framework
• HTML5 / CSS3 / JavaScript
• Bootstrap for responsive UI
• User-friendly, accessible design

Backend

• Python 3.8+
• Django ORM
• SQLite Database
• RESTful API endpoints

Machine Learning & Data Science

• scikit-learn for ML algorithms
• Pandas & NumPy for data processing
• Matplotlib & Seaborn for visualization
• Jupyter Notebooks for experimentation

ML Models Used

• Logistic Regression
• Random Forest
• ANN
• Ada Boost
• LGBM
• Decision Tree

Project Structure: Breast Cancer Segmentation System

mental_health_prediction/
├── activation-commands.txt
├── db.sqlite3
├── libraries.txt
├── manage.py
├── req-software.txt
├── requirements.txt
├── Mental Health Prediction/
│   └── [project files, including .ipynb notebooks, csv files etc. 
├── adminapp/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── tests.py
│   ├── views.py
│   ├── __pycache__/
│   └── migrations/
├── assets/
│   ├── static/
│   └── templates/
│       └── main/
│           └── index.html
├── mainapp/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── tests.py
│   ├── views.py
│   ├── __pycache__/
│   └── migrations/
├── media/
│   ├── correlation_heatmap.png
│   ├── dataset/
│   ├── plots/
│   └── profilepic/
├── userapp/
├── mentalenv/
├── mentalenv2/
│   └── lib/
│       └── python3.10/
│           └── site-packages/

System Performance

Metric	ANN	Logistic Regression	Ada Boost	LGBM	Decision Tree	Random Forest
Accuracy	75.26%	67.65%	76%	91%	98.18%	98.18%
Processing Time	0.2s	0.5s	0.4s	0.3s	0.1s	0.2s
Input Features	Behavioral & survey data (e.g., mood, activity, sleep, social interaction)
Model Size	50KB	500KB	200KB	100KB	30KB	80KB

Core Features

• Multi-Algorithm Prediction: Uses Logistic Regression, Random Forest, ANN, Logistic Regression, Ada Boost, LGBM, and Decision Tree for accurate mental health assessment
• Early Detection: Identifies signs of anxiety, depression, and burnout from behavioral and survey data
• User-Friendly Web App: Intuitive, accessible interface built with Django and Bootstrap
• Personalized Insights: Delivers tailored feedback, mood tracking, and actionable recommendations
• Data Visualization Dashboard: Visualizes predictions, trends, and model performance for better understanding

Why This Matters

• Mental health challenges often go unnoticed. Our system uses machine learning to enable early detection, empowering individuals to seek timely support and improve overall well-being.

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