Welcome to my GitHub! I'm Praveen, a passionate and driven software developer with a strong background in machine learning, deep learning, full-stack web development, game development, and systems programming. I love turning complex problems into elegant, efficient, and scalable solutions. My projects span across domains like education, healthcare, and artificial intelligence, with a keen interest in building impactful software.
| Degree | Institution | Year |
|---|---|---|
| Bachelor of Technology in IT | Anna University, CEG | 2021 - 2025 |
- Machine Learning: Supervised & Unsupervised Learning, Bayesian Optimization, Hyperparameter Tuning, Ensemble Methods
- Deep Learning: CNNs, RNNs, Transformers, Vision Transformers (DeiT), Multimodal Deep Learning
- Web Development: React.js, Node.js, Express.js, REST APIs, MySQL, MongoDB, JWT Auth
- Operating Systems: System-level programming, Memory management, Process scheduling
- Unity Game Development: 3D Game Mechanics, First-person controllers, ASD Therapy Tools
- Database Management: SQLite, MySQL, ER Diagrams, Query Optimization
- Version Control: Git, GitHub, GitLab
- Others: Data Visualization, UI/UX Design, Agile Methodologies
A full-stack educational web application built with React.js and Node.js. Features include:
- Secure authentication using JWT and bcrypt
- Role-Based Access Control (RBAC)
- RESTful APIs and real-time data visualization
- MySQL as backend database
Built multiple ML and DL models to classify autism spectrum disorder from clinical and behavioral data:
- Applied SVM, Decision Trees, XGBoost, LightGBM, Random Forests
- Integrated deep learning models with multimodal datasets (e.g., tabular + image/text)
- Used Bayesian Optimization and Grid/Random Search for hyperparameter tuning
- Explored Transformer-based models and Vision Transformers (DeiT) for high-dimensional features
- Achieved robust evaluation through k-fold cross-validation and balanced metrics
Implemented various machine learning models for fetal risk classification:
- Models used: SVM, Decision Trees, Random Forest, Bagging, Boosting, CNN, FNN
- Achieved high accuracy and sensitivity across multiple metrics
Developed a responsive and visually appealing website for showcasing AI research:
- Built with React.js
- Emphasized modular components and clean UI
- Designed for public visibility and information sharing
Developing a Unity-based 3D game aimed at children aged 4–14 with Autism Spectrum Disorder (ASD):
- Features therapy tasks like Matching Pairs, Joint Attention Training
- Includes voice-based interaction and eye gaze direction for social learning
- Game modes: Easy (4x4), Medium (5x5), Hard (6x6)
- Full 3D first-person view with player movement and interactive environment
| Category | Tools/Technologies |
|---|---|
| Languages | C++, Python, JavaScript, C#, SQL |
| Frontend | HTML5, CSS3, React.js, Tailwind CSS |
| Backend | Node.js, Express.js |
| Databases | MySQL, SQLite, MongoDB |
| ML/DL | Scikit-learn, TensorFlow, Keras, PyTorch |
| Transformers | HuggingFace, Vision Transformers (DeiT), BERT |
| Optimization | Optuna, Hyperopt, Grid Search, Random Search, Bio inspired optimizers |
| Game Dev | Unity3D, C# |
| Tools | Git, GitHub, Postman, VS Code |
| OS | Linux, Windows, Shell Scripting |
- Email: praveenanand333@gmail.com
- LinkedIn: https://www.linkedin.com/in/praveenveerachamy/
I'm always open to collaborating on impactful projects, especially in the fields of healthcare tech, educational tools, and AI applications. If you have an idea or want to contribute together, feel free to reach out!
This repository is open-source and available under the MIT License.
This README serves as a digital portfolio. Feel free to explore the repositories and connect with me!