As an aspiring data scientist and blockchain enthusiast, I, Sai
Rahul Guggilam, am passionate about leveraging cutting-edge
technologies to solve complex problems. My journey in artificial
intelligence and data science has led me to develop diverse
projects, from implementing advanced object detection models using
YOLOv5 to creating innovative laptop price prediction systems.
I've also ventured into the realm of blockchain, orchestrating a
secure document management system using Web3 technologies. My
experience spans machine learning, computer vision, and
decentralized systems,. With a strong foundation in programming
languages like Python, C, and Solidity, I'm equipped to tackle
challenges across various domains. My dedication to continuous
learning is evident through my numerous certifications and
achievements, including tackling over 800 problems on LeetCode. As
I approach the completion of my Bachelor's degree in Artificial
Intelligence and Data Science, I'm excited to contribute my skills
and knowledge to push the boundaries of what's possible in data
analytics, machine learning, and blockchain technology.
This Project focuses on object detection the use of the deep mastering algorithm YOLOv5, an up-to-date model of YOLO that gives multiplied accuracy and performance.
It is a Streamlit application of the YOLOv5 model, which is a most widely used object detection algorithm.
The laptop price prediction is a machine learning model for predicting laptop prices.
It's a regression model that uses various features to accurately estimate prices.
The project demonstrates expertise in data analysis, machine learning algorithms, and repository management.
It's an effective example of a well-structured and useful data model.
A document management system employing the Inter PlanetaryFile System (IPFS) and block-chain facilitates trusted sharing of sensitive data via user-defined access controls without intermediaries.
The project color detection using Open-Cv is an application built with Python, OpenCV, and Pandas.
It allows users to upload an image and automatically detect the name of the color by clicking on it.
This lookup focuses on object detection the use of the deep mastering algorithm YOLOv5, an up-to-date model
of YOLO that gives multiplied accuracy and performance.
A document management system employing the Inter PlanetaryFile System (IPFS) and block-chain facilitates
trusted sharing of sensitive data via user-defined access controls without intermediaries.
This project introduces an automated system for generating certificates based on user-provided Excel files. Leveraging a Flask- based front-end interface, users can effortlessly
upload Excel sheets containing participant details such as names, IDs,
and email addresses. The system seamlessly parses this data and dynamically generates personalized certificates.