Bhargav Krishna Yaramachu
Career Objective
Java Full Stack Developer with 0 years of experience building full-stack Java applications and machine-learning prototypes through academic projects. Skilled in Java, Spring Boot, REST APIs, React, HTML, CSS, JavaScript and MySQL, with hands-on use of JWT authentication, AWS S3 object storage and SendGrid for email notifications. Delivered an end-to-end Hospital Management System using Spring Boot backend, MySQL persistence, React frontend and S3-backed document storage. Built a Diabetes Prediction System using Python, Pandas and scikit-learn to preprocess clinical data and evaluate classification models. Experienced with Git and GitHub for source control and maintaining reproducible code and tests. Seeking an entry-level Java Full Stack Developer role to apply Spring Boot and React experience while expanding cloud and data engineering skills.
Education
Technical Skills
Projects
- Architected and implemented a Spring Boot backend to model hospital workflows including patient registration, doctor management, appointment scheduling, and bed allocation.
- Developed RESTful APIs for patient and appointment services and integrated MySQL for transactional persistence and data integrity.
- Engineered JWT-based role authentication to secure Admin, Doctor, and Receptionist access flows and enforce authorization rules.
- Built a responsive React frontend using JavaScript to provide appointment scheduling and staff dashboard interfaces.
- Designed responsive layouts with HTML and CSS to ensure cross-device compatibility for clinicians and reception staff.
- Preprocessed and cleaned patient health datasets using Python and Pandas to handle missing values, outliers, and feature normalization.
- Implemented model training pipelines with scikit-learn to evaluate logistic regression, SVM, naive Bayes, and decision tree classifiers.
- Engineered clinical features and applied cross-validation to compare model generalization and select the best-performing approach.
- Tuned hyperparameters and measured model accuracy, precision, and recall to improve prediction quality and reduce false positives.
- Serialized the final model and documented a reproducible training workflow for future retraining and deployment.
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