A research-oriented platform that leverages Artificial Intelligence (AI) and Machine Learning (ML) to explore and analyze mental health data, providing insights and tools for research, prediction, and visualization.
The Mental Health AI Research Platform enables users to preprocess data, train models, and visualize outcomes related to mental health studies.
It bridges the gap between AI research and mental wellness, allowing data-driven insights through an interactive, research-friendly interface.
- Data preprocessing and cleaning pipeline
- Interactive data visualization dashboard
- Machine Learning model training and evaluation
- Predictive analytics and insights visualization
- Model performance metrics (accuracy, confusion matrix, ROC curve)
- Export results for reports and publications
- Simple and user-friendly UI for researchers
Frontend: React, Next.js, Tailwind CSS
Backend: Python (Flask / FastAPI)
AI & ML: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, OpenCV
Database: MySQL / MongoDB
Tools: Git, GitHub, VS Code, Jupyter Notebook, Power BI
Operating Systems: Windows, Linux
# Clone the repository
git clone https://github.com/Akashyadav-aiml/Mental-Health-AI-Research-Platform.git
cd Mental-Health-AI-Research-Platform
# Backend setup
cd backend
pip install -r requirements.txt
# Frontend setup
cd ../frontend
npm install