Priyank Shah

Experienced Java Backend Software Engineer with a strong background in developing scalable applications and ensuring comprehensive testing. Proficient in designing and optimizing backend systems using Java and related technologies. Eager to apply expertise in backend development to deliver efficient, high-performance solutions in a collaborative environment.

TravellingAgency Website

July,2024 - Currently
Technologies: Spring Boot, Thymeleaf, MongoDB, JavaScript, Amadeus API, PayPal API

• Developed a hotel booking platform where users can search and book hotels by city using the Amadeus API.
• Integrated PayPal for secure payments and implemented an activity discovery feature to explore things to do within 20km of the city.

Product Management System with Hibernate
Technologies: Hibernate, PostgreSQL, JSP, Servlet

• Developed a product management system that implements full CRUD operations (add, edit, delete, and search products) using Hibernate ORM.
• Integrated PostgreSQL as the database backend, with JSP and Servlets for the user interface, providing seamless data management.

Event Participation Web Application
Technologies: Servlet, JSP, PostgreSQL

• Created a dynamic web application with an event participation form for users to submit entries.
• Implemented CRUD operations for managing and searching event submissions by ID, using PostgreSQL as the database backend.

Quiz Application
Technologies: React, Vite, JavaScript

• Developed a dynamic quiz app with multiple subjects where users answer MCQs and receive a final score.
• Implemented a responsive UI for navigating questions, viewing results, and retaking quizzes, offering real-time score calculation.

Contracker App

Boston University (Jan 2018 - May 2018)
Technologies: Android, Firebase

• Created an app connecting customers with contractors for job postings.
• Integrated Firebase for real-time data synchronization between contractors and customers.

Time Series Prediction & Stock Market Prices

Boston University (Jan 2017 - May 2017)
Technologies: LSTM, Python

• Analyzed stock market trends using LSTM and compared results with DeepMind's WaveNet.
• Preprocessed financial data to improve prediction accuracy.

YouTube Recommendation Link Prediction

Boston University (Sep 2016 - Nov 2016)
Technologies: Python, Machine Learning

• Developed a recommendation system using binary classification to predict related video links on YouTube.
• Analyzed and compared video categories to optimize recommendations.

Prediction for Star Ratings in User Reviews

Boston University (Sep 2016 - Nov 2016)
Technologies: Python, K-Means Clustering

• Built a model to predict user review star ratings based on text data.
• Implemented K-Means clustering and term frequency vectorization to classify reviews.

• Received a Bright Spark Award for 4th Quarter of 2020 from Infostretch Corporation for my work.