🌱 On the way of learning Advanced Machine Learning & Data Science.
🎓 I’m an Open-Source enthusiast & a Masters student pursuing my degree in Big Data Analytics at San Diego State University.
👨‍💻 I am passionate about Natural Language Processing, Data Science Machine Learning, DevOps & I enjoy learning new things.

• Associate Software Engineer, Temenos (2021-2023): Contributed to the Infinity software suite, specializing in front-end development and integration. Utilized JavaScript, HTML, CSS, and Visualizer tool for screen development, and employed Figma for design. Enhanced project workflows through integrations with BitBucket.
• Graduate Student Assistant/Data and Analytics Intern at SDSU ZIP Launchpad: Recently commenced role focusing on leveraging data analytics to drive insights and innovations. Engaged in a hands-on environment to apply analytical skills in real-world projects, supporting the dynamic startup ecosystem at ZIP Launchpad.
• Participant, American Astronautical Society CanSat Competition (Fall 2020): Actively engaged as an Electronics Engineer in this prestigious annual design-build-launch competition focused on space-related projects. Successfully cleared the preliminary round, contributing to the team by leveraging Raspberry Pi for live streaming and sensor data collection. Analyzed ground station data to support the end-to-end life cycle of our complex engineering project, from conceptual design to post-mission analysis, highlighting practical skills in system operation and data analysis in a competitive, real-world scenario.
• University Innovation Fellow, Stanford d.school:UIF Completed a 6-month Design Thinking course; attended Silicon Valley Meetup, Fall 2022. Enhanced skills in innovative design and collaborative problem-solving.

• Authored a groundbreaking paper titled "Human Emotion Classification using KNN Classifier and Recurrent Neural Networks with Seed Dataset," published in the IEEE Xplore Digital Library. The study introduces an innovative approach to emotion classification using EEG signals, achieving remarkable accuracy rates of 96.22% with KNN classifiers and 85.71% with Recurrent Neural Networks (RNNs). This research is pivotal for advancements in product review analysis and healthcare, particularly in identifying depression. Published link
• Received recognition for my contribution to the field of financial security with the publication "Detection of Credit Card Fraudulent Transactions using Boosting Algorithms" in the Journal of Emerging Technologies and Innovative Research (JETIR). This work evaluates the effectiveness of CatBoost, XGBoost, and Stochastic Gradient Boosting algorithms in detecting credit card fraud, demonstrating the superior performance of the CatBoost algorithm across various metrics. The publication is an essential addition to the domain of digital financial transactions security. Published Link