Interdisciplinary research in collaboration with the CSRTC, focusing on integrated approaches for impurity detection and classification in solid foods using machine learning and NIR spectrometry.Designed a robust framework leveraging transfer learning to enhance the detection and classification of skin diseases, achieving a validation accuracy of 96.46%on datasets including the Monkeypox Skin Images Dataset (MSID).Supported and mentored peers by guiding them efficient techniques to carry out researches, and collaborating on research methodologies.Authored and presented findings at prestigious conferences.

Gained hands-on experience in applying machine learning tools, techniques & algorithms to solve real-world problems.Developed and optimized machine learning models for Customer Churn Prediction and conducted data-driven insights for World Cup 2023 Analysis.

Training-based internship focused on Oracle SQL and PL/SQL, enhancing database management and query optimization skills.Gained hands-on experience by contributing to a live project, developing an Employee Payroll Management System to streamline payroll processes and improve operational efficiency.