🎓 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.

• 🗂 Project & Idea Management

  • Create, edit, and organize multiple content projects
  • Structured workflow tracking (Idea → Draft → Production → Published)

• 📝 Script & Notes Editor

  • Dedicated writing space for scripts
  • Asset linking within projects

• 📅 Content Planning System

  • Publishing schedule tracking
  • Workflow stage monitoring

• 📦 Local Database Integration

  • SQL.js-powered persistent storage
  • Fully offline data management

• 🎨 Modern UI/UX

  • Glassmorphism-inspired interface
  • Smooth animations using Framer Motion
  • Responsive and clean layout

• 🖥 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)

GitHub Repository

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.