🎓 B.Tech in Artificial Intelligence & Machine Learning
💻 Full-Stack Python Developer | Machine Learning | NLP
🚀 Passionate about building end-to-end ML-powered web applications
📈 Focused on real-world, deployable systems, not just models
- Developing full-stack ML applications (Frontend + Backend + ML)
- Integrating Machine Learning models with RESTful APIs
- Building React-based frontends for ML systems
- Designing scalable backend services for inference & data flow
- Preparing for ML Engineer / Python Backend / Full-Stack roles
A local-first desktop application built for content creators to manage ideas, projects, scripts, assets, and publishing workflows — all in a secure offline environment.
CreatorTank is designed to streamline the creative pipeline from idea generation to content publishing.
Unlike cloud-based tools, it stores data locally, ensuring privacy, speed, and full user control.
This project demonstrates strong architectural thinking, state management, desktop app integration, and modern frontend engineering practices.
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🗂 Project & Idea Management
- Create, edit, and organize multiple content projects
- Structured workflow tracking (Idea → Draft → Production → Published)
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📝 Script & Notes Editor
- Dedicated writing space for scripts
- Asset linking within projects
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📅 Content Planning System
- Publishing schedule tracking
- Workflow stage monitoring
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📦 Local Database Integration
- SQL.js-powered persistent storage
- Fully offline data management
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🎨 Modern UI/UX
- Glassmorphism-inspired interface
- Smooth animations using Framer Motion
- Responsive and clean layout
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🖥 Cross-Platform Desktop App
- Built using Electron
- Runs on Windows, macOS, and Linux
- Electron — Desktop application framework
- React + Vite — Modern frontend tooling
- TypeScript — Type-safe development
- SQL.js — Client-side database
- Framer Motion — UI animations
- Lucide React — Icon system
- Demonstrates full-stack desktop architecture (Frontend + Local DB + Electron main process)
- Applies local-first software design principles
- Solves a real productivity problem for content creators
- Built with scalability in mind (future features: tagging, versioning, templates)
Tech: Python, NLP (TF-IDF), Logistic Regression, Flask, REST APIs
- Built an NLP pipeline to classify feedback as Positive / Neutral / Negative
- Improved negation handling using bigram TF-IDF features
- Addressed class imbalance with class-weighted learning
- Combined multiple feedback text fields using Git feature branching
- Built a multi-input web interface aligned with training logic
- Documented model limitations and experimentation strategy
- Python
- JavaScript
- HTML5, CSS3
- React (component-based UI, hooks fundamentals)
- Responsive UI design
- State & form handling
- Frontend–backend integration
- API consumption (fetch / axios)
- Flask (routing, forms, REST APIs)
- RESTful API design principles
- Model inference endpoints
- Request validation & error handling
- Backend–ML integration
- Basic deployment concepts (Gunicorn)
- Scikit-learn
- Pandas, NumPy
- Regression & Classification models
- NLP (TF-IDF, Logistic Regression)
- Model evaluation & explainability
- MongoDB Atlas
- Git & GitHub (branching, version control)
- VS Code
📧 Email: basudevd983@gmail.com
💼 LinkedIn: https://www.linkedin.com/in/basudev-das
💡 Focused on building scalable, ML-driven web systems using modern frontend and backend engineering practices.
