Hi, I'm

Shivam Prasad

AI & Blockchain Developer

Transforming ideas into intelligent solutions through AI, ML and Blockchain technology

AI/ML
Blockchain
Full-Stack
Cloud

About Me

I'm a tech enthusiast passionate about AI&ML, and blockchain. I love creating innovative projects like emotion-based assistants and NFT marketplaces. Always eager to learn, I enjoy solving challenges and turning ideas into real solutions. Hackathons excite me and push me to explore new possibilities. My goal is to build impactful technology that makes a difference.

Expertise

My expertise spans across AI, Blockchain, Full-Stack Development, and Cloud technologies. I excel in building innovative solutions and enjoy tackling challenging projects.

Projects

MoodifyAI

A sophisticated system using VGG architecture for real-time human emotion recognition and personalized music recommendations.

TensorFlow Python VGG

NFT Marketplace

A decentralized marketplace for trading NFTs with smart contract integration and secure transactions.

Solidity Web3.js React

EchoHive

A real-time chat application built with Flask and Socket.IO that allows users to communicate across different language barriers through instant message translation.

HTML CSS python Flask

maleVSfemale_classication

This project aims to classify images based on gender (male or female) using deep learning techniques. The model can be used in applications like demographic studies, personalized marketing, and image-based analytics.

TensorFlow Python CNN

WishGenie

WishGenie is a web application that helps users generate personalized wishes for various occasions such as birthdays, holidays, and celebrations. This platform allows users to craft thoughtful and unique messages easily.

HTML CSS JS Python

Generate a CSS Color Gradient

The "Generate-a-CSS-Color-Gradient" tool is a web application that helps users generate custom CSS gradients by selecting colors and adjusting various settings.

HTML CSS JS

Image Classification

the fundamentals of image classification using a small dataset of 44 images across 3 classes. Due to the limited dataset size, the current model achieves an accuracy of approximately 29%.

Python Jupyter TensorFlow Machine learning

Emotion Recognition

model classifies facial expressions into seven classes: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The model is built with TensorFlow and leverages transfer learning with the pre-trained VGG16 architecture

Python TensorFlow CNN VGG16

E-commerce App using AI

An AI-enhanced e-commerce platform that personalizes shopping experiences with recommendation algorithms and dynamic pricing.

React Python TensorFlow

Skills

Core Technologies

TensorFlow Deep learning HTML, CSS, JavaScript NLP Hugging Face Solidity Python

Other Technologies

Docker Kubernetes React APIs Web3

Get In Touch

I'm always interested in hearing about new projects and opportunities.