Hi, I am Shivanand Racha
I'm a Full-Stack Developer passionate about Machine Learning and building secure, intelligent applications.

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About me

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I'm a final-year B.Tech IT student passionate about building real-world solutions through Machine Learning and Full-Stack Web Development. With a strong grasp of the MERN stack, RESTful APIs, and DBMS, I enjoy creating secure, responsive apps using technologies like React, FastAPI, and PyTorch.

I’m particularly interested in privacy-preserving AI systems and modern web architectures. I thrive in collaborative environments and aim to develop tech that’s not just powerful and scalable, but also ethical, intelligent, and user-friendly.

View Resume

Education

Sastra Deemed University, Thanjavur, TN

Bachelor of Technology in Information Technology

Oct 2022 – June 2026

GPA: 7.62 / 10

SR Edu Centre, Warangal, TS

Intermediate Education – MPC

June 2020 – May 2022

Grade: 98.4%

Skills

Languages

Python JavaScript Java C++ SQL

Frameworks & Libraries

React Node.js Express Django

Tools & Platforms

Git GitHub VS Code Postman

ML Libraries

PyTorch NumPy Scikit-learn Matplotlib Pandas

Projects

WanderLust

  • Developed a global hotel booking platform with a sleek UI and responsive design.
  • Built using HTML, CSS, JavaScript, Node.js, and Express.js.
  • Implemented advanced filtering with location, price, ratings, options and integrated Google Places API for location autocomplete.
  • Added backend search with pagination for performance. Designed a flexible booking data schema to support future features.
Source Code

TradeMate

  • Built a full-stack stock trading platform using HTML, CSS, JavaScript, Node.js, Express.js, and MongoDB, with real-time stock chart visualization.
  • Integrated a Python-based machine learning model using scikit-learn to analyze historical stock data and predict short-term price trends.
  • Developed a RESTful API to serve ML predictions, enabling dynamic and personalized trade suggestions on the frontend.
  • Structured the backend for scalability with modular API design, middleware-based request handling, and role-based access control.
  • Implemented real-time data updates and seamless user interaction through WebSocket integration and efficient state management.
Source Code

Fruit Quality Detection

  • Designed and deployed deep learning models (SqueezeNet, ResNet, ShuffleNet, MobileNet) for fine-grained image classification of fruit quality across multiple categories.
  • Utilized transfer learning on pre-trained CNN architectures with custom classification heads, achieving high accuracy on diverse and imbalanced datasets.
  • Enhanced model generalization through data augmentation, early stopping, and learning rate scheduling during training.
  • Optimized and quantized models for efficient, real-time inference on edge devices with limited computational resources.
  • Deployed end-to-end inference pipeline, enabling robust offline classification in resource-constrained environments.
Source Code

Contact

Interested in collaborating, hiring, or discussing cool projects? Let's connect!