@yeltibhavithreddy

 

Yelti Bhavith Reddy

Software Engineer

Walmart USA Advanced Softwar...

Hyderabad

I am Yelti Bhavith Reddy, a recent Computer Science graduate from MallaReddy University, Hyderabad, with expertise in Java, Python, and machine learning. I am passionate about innovation and eager to contribute to cutting-edge technology projects in the entrepreneurial community.

Connect

Open to : Collaborate
Looking for : Job , Internship

Skills & expertise

 

Python, Machine Learning, SQL, Java, Git, Docker ,HTML CSS 

Experience

 
Software Engineer
Walmart USA Advanced Software Engineering Virtual Experience Program on Forage

Completed the Advanced Software Engineering Job Simulation where I solved difficult technical projects for a variety of teams at Walmart. Developed a novel version of a heap data structure in Java for Walmart’s shipping department,

showcasing strong problem-solving and algorithmic skills. Designed a UML class diagram for a data processor,

considering different operating modes and database connections. Created an entity relationship diagram to design a

new database accounting for all requirements provided by Walmart’s pet department.

Intern
AICTE Eduskills Machine Learning Virtual Internship

 Participated in a virtual internship program facilitated by the All India Council for Technical Education (AICTE) in collaboration with Eduskills.

Products & Projects

 

On-Chip Epilepsy detection using Machine learning.

Implemented a real-time seizure detection system on a low-power hardware platform by integrating machine learning algorithms directly onto an embedded device. Collected and processed electroencephalogram (EEG) signals for feature extraction, trained machine learning models for seizure detection, and optimized the implementation for on-chip operation

Credit card fraud detection using Autoencoders.

Designed and implemented a credit card fraud detection system using autoencoder neural networks. Leveraged unsupervised learning techniques to detect anomalous patterns in credit card transactions indicative of fraudulent activity, contributing to the development of robust fraud detection algorithms