SUCHARAN NARRA

Around 2 Years of IT experience working with various SDLC methodologies-Agile, Scrum, Waterfall Model and applied Agile Test Practices SCRUM, Scripted Test Cases for TDD methodologies.

Experience in Design, Development, Implementation and Testing of applications using Java/J2EE. Developed modules using Angular, Node.js, Reacts, Bootstrap, JavaScript, Ajax, jQuery, HTML5 and CSS3.

Experience in using JavaScript, jQuery, AngularJS, Angular 4/8, Type script for enhancing clientside user experience and improve performance.

Strong experience in developing applications using Core Java concepts like OOPS, Multithreading, Collections Frameworks, Exception Handling, Data structures and JDBC.

Experience in working with NoSQL databases like MongoDB. Experienced in implementing Service Oriented Architecture and Web Services using SOAP, REST.

Experience in database modeling, design and development of PL/SQL stored procedures, packages in relational databases Oracle 9i /10g /11g/12c and MYSQL.

Hands on experience using Pandas and Git Respositories. Recognized for collaborative problem-solving and meticulous attention to detail, I am eager to contribute my technical expertise to software development projects and drive continuous professional growth.

STUDENT DATABASE MANAGEMENT SYSTEM

Developed a dynamic web application using PHP for server-side scripting, HTML for markup, and MySQL for relational database management. 

Implemented responsive design with JavaScript to enhance user interaction and ensure cross-device compatibility. 

Integrated a robust authentication system to manage user roles and enforce access controls securely.

 Employed server-side scripting techniques for data validation and security measures. 

Leveraged Git for version control, facilitating collaborative development and code management. Integrated APIs to enable real-time data updates and retrieval from external sources. 

FAKE NEWS DETECTION USING MACHINE LEARNING

 Created an advanced fake news detector using a special type of artificial neural network called Recursive Neural Network (RNN). 

This helps the system understand complex patterns in text better. 

Picked RNN over traditional networks because it's really good at handling information that comes in a sequence, making our accuracy much better.

 Used Python for the project because it's versatile and has lots of tools to help with development. Included machine learning algorithms to teach the system based on labeled data, allowing it to adapt to changes in fake news patterns.

 By using RNN, our system got better at understanding the context and timing in news articles, making it more accurate in spotting deceptive content. 

MOVIE AWARD PREDICTION USING MACHINE LEARNIG

 Identified the need for a fake news detection system to address misinformation. 

Chose Recursive Neural Network (RNN) for its ability to understand intricate patterns in textual data. 

Designed the model architecture, focusing on implementing RNN to enhance pattern recognition. 

Selected Python as the programming language for its versatility and rich libraries.

 Integrated machine learning algorithms to train the model using labeled data, allowing adaptation to evolving patterns.

 Executed the project step by step, utilizing Python tools for the implementation of the RNN-based fake news classifier. 

Conducted rigorous testing to ensure the accuracy and reliability of the fake news detection system. 

Observed that the adoption of RNN improved the system's understanding of context and temporal dependencies in news articles, enhancing accuracy in identifying deceptive content.