parikshitjanagama Goto Github PK
Name: Parikshit Janagama
Type: User
Company: Software Developer
Bio: Working as a software developer since Aug 2018. Studied Masters in Computer Science at University of Texas at Dallas.
Location: Dallas,Tx
Name: Parikshit Janagama
Type: User
Company: Software Developer
Bio: Working as a software developer since Aug 2018. Studied Masters in Computer Science at University of Texas at Dallas.
Location: Dallas,Tx
● Designed and implemented database for contact Manager by creating user interface using java swings ● Implemented stored procedures and views to insert, update, delete a contact form the database using java MySQL.
Created a manual Database engine that is loosely based on MySQL which supports high level commands. Optimized the query processing and data retrieval by implementing the concepts of Index files using data Structures
● Designed a website utilizing Service-Oriented MVC architecture, RESTful Web Services, JSP, AJAX, jQuery, HTML, CSS,JavaScript,Bootstrap. ● Implemented user registration and login, ability to bid and post, search for a bid, filtering of bids and add the desired bid to the cart. The client- server communication is TLS/SSL encrypted and gZip compressed ● Implemented ORM framework with Hibernate and Single-Sign on using SAML. Implemented Mem Cached for caching
Few Algorithms and Coding challenges that I have worked on
● Created a single web page application, Using ReactJS, which has the functionalities like Sticky notes that allows user to add , edit , save and delete a note
Java Collections Run Time time complexities.
● Performed sequential and parallel analysis on the Wikipedia page-view logs to analyze page-view trends and derive the total average page views per day, top trending topics etc
• Developed a Recommender System for restaurants by performing analysis on data preprocessed from Yelp Dataset. • Used Altering Least Squares method with Matrix Factorization and Neighborhood Model to train and build the Recommender System. • Tested the Recommender System with multiple rounds of Cross Validation technique and 16% prediction error is observed
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