I'm a B.Tech CSE student at Graphic Era Hill University, driven by a love for building innovative solutions through code. Passionate about building scalable web applications and diving deep into machine learning and AI. I love transforming complex problems into elegant, efficient solutions through code. Whether it's crafting modern UI/UX with React, building robust backends with Node.js or Django, or training ML models for real-world impact—I'm all in.
- 🌱 Currently mastering the React.js + Python (Flask/Django) + MongoDB/MySQL/PostgreSQL
- 🔍 Exploring Artificial Intelligence & Machine Learning applications
- 💻 Building projects that solve real-world problems with clean, efficient code
- 🚀 Open to collaboration on innovative development projects
- 📧 Reach me at: tanujbhatt8279@gmail.com
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Graphic Era Hill University, Bhimtal (2023-2027), CGPA = 9.63 Core focus: Data Structures & Algorithms, Web Development, AI/ML |
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Saraswati Academy, Haldwani (2022), Percentage = 95.6% Physics, Chemistry, Mathematics, English and Computer Science |
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Saraswati Academy, Haldwani (2020), Percentage = 96% English, Hindi, Mathematics, Science, Social Science, and General Knowledge |
- Built C++ order book management system processing over 10,000+ trade entries from CSVs for real-time analysis.
- Designed a matching engine executing simulated buy/sell trades in under 5ms, enabling fast decision making.
- Used Dear ImGui to develop a GUI with 10+ dynamic modules for order input, analytics, and status logs.
- Leveraged Vulkan to render high-performance graphics, achieving 60+ FPS rendering on mid-tier hardware.
- Used CMake for modular architecture, reducing build complexity by 40% & improving cross-platform deployment.
- Separated UI and logic layers (2000+ LOC), ensuring maintainability, testability & future expansion (real-time feeds).
Technologies: C++, Dear ImGui, Vulkan, CMake, MySQL
- Developed an ML pipeline to analyze 1,000+ professional emails by implementing custom TF-IDF Vectorizer, spam classifier (Naive Bayes), and sentiment analysis (VADER).
- Stored email metadata and predictions using MongoDB, enabling interactive analysis and model evaluations.
- Deep learning (LSTM, BERT), spaCy-based NER, and LDA topic modeling for workload and email clustering.
- Activity trend predictions (ARIMA, LSTM) and delivery of a visualization dashboard to track burnout and meeting effectiveness.
Technologies: Python, scikit-learn, MongoDB, Flask, NLTK, Gensim, spaCy, React.js, CSS
- Built a scalable E-Commerce website with React.js, supports real-time product search, cart, and checkout features.
- Designed secure order flow by integrating a custom AES-128 encryption system in C++ for delivery data.
- Encrypted QR codes with user and agent details, enabling role-based decryption only by verified agents.
- Structured backend with Flask & MongoDB across two-tier databases to manage user, agent, and order collections.
- Enabled real-time delivery traceability by logging every access event, securing over 50+ test orders end-to-end.
Technologies: React.js, CSS, Flask, MongoDB, AES - C++
- Built an intuitive web app to create and simulate flowcharts for algorithms, logic, and system workflows.
- Users can drag, drop, and connect nodes in real-time using a custom-built React.js canvas editor.
- Enabled dynamic simulation of logic flow with step-by-step tracing, aiding in visual debugging and teaching.
- Backend in Flask stores and retrieves user-generated diagrams, allowing persistent project sessions.
- Supports exporting diagrams as SVGs/PNGs and sharing via unique links, improving collaboration.
Technologies: React.js, CSS, Flask, MongoDB
💡 Always exploring new technologies and pushing boundaries in software development!