Snehanath Pabbisetty

INTRODUCTION

Hello! I’m Snehanath Pabbisetty, an enthusiastic and driven Computer Science undergraduate specializing in Artificial Intelligence and Machine Learning (AIML). I’m deeply passionate about harnessing the power of AI, cloud technologies, and software development to build innovative, real-world solutions that make a meaningful impact.

Throughout my academic journey and project experiences, I’ve always believed in continuous learning and staying curious. Whether it's diving into a new framework, leading a team at a hackathon, or guiding peers through AI concepts, I strive to create, collaborate, and contribute to the dynamic tech ecosystem around me.

TECHNICAL SKILLS

Current Skills:

Programming Languages: Python, C, SQL, HTML, CSS, R (Python integration)

Databases: MongoDB (Introduction-level proficiency)

Cloud & Networking: AWS Academy Cloud Foundations, Juniper Networking Cloud Essentials

Machine Learning Techniques: Random Forest, Support Vector Machines (SVM), Data Visualization, Predictive Modeling

Planning to Explore:

Deep Learning: Concepts like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)

Advanced AI & Data Science tools: TensorFlow, Keras, Matplotlib, advanced Pandas

Cloud DevOps: AWS Lambda, serverless applications, CI/CD pipelines

Domain Applications: Financial fraud detection using AI — currently reviewing the latest research papers and methodologies in this area.

PROJECTS I WORKED ON
1️⃣ CKD Predictor A healthcare AI project where I developed a predictive model for Chronic Kidney Disease (CKD) detection using Random Forest and SVM algorithms. By integrating clinical, genetic, and imaging data, the model improved early detection accuracy and delivered personalized insights. Additionally, I optimized project workflows to enhance operational efficiency by 25% — reflecting my focus on both performance and process improvement.

2️⃣ Kidney Disease Analyzer This analytical tool evaluates the significance of various clinical attributes in the onset of kidney disease. It helps identify the most impactful biomarkers, enabling timely medical intervention and informed decision-making.

3️⃣ Resume Builder A dynamic web-based application designed to simplify resume creation. It allows users to input details, select from multiple layouts, and generate polished, professionally formatted resumes tailored to different job profiles — enhancing accessibility and ease for students and early-career professionals.

4️⃣ UPI Fraud Detection System A fintech-focused AI project where I developed a Fraud Detection Model for UPI transactions using the XGBoost algorithm. This model was trained on transactional data to identify anomalous patterns and flag potential fraudulent activities in real time with high accuracy.

To offer a user-friendly, interactive interface, I designed a React-based frontend that displays transaction details, fraud alerts, and real-time risk scores. The system ensures both scalability and security, serving as a prototype for integrating AI-powered fraud detection into modern digital payment platforms.

Tech Stack:

Backend: Python, XGBoost, Pandas

Frontend: React.js

CERTIFICATIONS

Google: AIML Virtual Internship — gained foundational insights into AI problem-solving workflows.

AWS: AWS Academy Cloud Foundations — introduced to core AWS services, cloud concepts, and architecture best practices.

Juniper: Networking Cloud Internship — explored networking fundamentals in cloud environments.

MongoDB University: Introduction to MongoDB — acquired basic skills in working with NoSQL databases.

ASPIRATIONS In the coming years, I aim to:

Build AI-powered applications that address pressing challenges in healthcare, finance, and enterprise systems.

Contribute to financial fraud detection systems leveraging machine learning and deep learning methodologies.

Dive deeper into cloud-native development and DevOps to ensure reliable, efficient, and scalable deployments for AI models and applications.

Beyond technical goals, I aspire to mentor and lead communities by sharing knowledge, conducting workshops, and fostering a culture of innovation, much like my previous engagements at IEEE hackathons and technical societies.