Balisetty Prasanth

Accomplished professional with expertise in Python programming, problem-solving, and machine learning algorithms. Proficient in utilising cloud platforms and data analysis techniques to drive innovative solutions. Experienced in Natural Language Processing (NLP) and adept at using GitHub version control for efficient project management. Committed to leveraging technical skills to contribute to cutting-edge projects and advance career in the field of data science.

AWS Cloud Practitioner

Google Cloud Certified Associate Engineer
AWS Solution Architecture

"Emotion Speech Recognition in the Cloud"

IDICOT 2024 – IEEE Conference

Built a cloud-based system to recognize emotions in speech using machine learning and NLP techniques.

Credit Card Fraud Detection Using Machine Learning in Python"

ICMCSI 2024 – IEEE Conference

Developed a fraud detection system using machine learning with a focus on reducing false positives and handling class imbalance.

Student PeerMentor- KL University Vaddeswaram, India Technical Events Coordinator July 2022 – November 2023 Facilitated one-on-one and group mentoring sessions for over 100 students, leading to a noticeable 15 percent improvement in their academic performance and a 25 percent increase in class participation rates for subjects Mern Stack Web Development, Python Full Stack Web Development with Django, Automata Theory and Formal Languages, Advanced Computer Architecture, Cloud Infrastructure and Services. BroadBand Networks and 5G Connections Club Tech Team July 2023 – Dec 2023 Participated in club tech events and led sessions for students, fostering knowledge sharing and engagement within the community.

• Credit Card Fraud Detection: Improved fraud detection accuracy by developing class-imbalance resilience techniques and implementing adaptive machine learning models to address highly imbalanced datasets.
• Speech Emotion Recognition: Designed a model to detect a range of emotions in speech —including happiness, sadness, anger, and fear—enhancing the ability of systems to interpret user emotional states.
• Multimodal Emotion Recognition Framework: Built a deep learning system combining audio and visual inputs for emotion analysis, integrating CNNs for facial expression recognition and RNNs for voice tone analysis to improve emotional prediction accuracy.

• Bidding System Platform (React.JS): Developed a dynamic web application for real-time product and service bidding using React.JS , enabling users to interact with contextual pricing and offer updates.
• Student Learning Management System (Java Full Stack): Engineered a microservices-based platform for educators to manage courses, assignments, and assessments efficiently. Enhanced content delivery and access through a centralized, scalable architecture.

• AI Image-Generating Telegram Bot: Built a Telegram chatbot integrated with AWS Bedrock to generate custom AI images from user prompts, leveraging cloud-based ML services for scalable performance and rapid image synthesis.
• Utilized AWS Lambda, S3, and API Gateway to ensure automation, scalability, and efficient data flow across cloud components.