I, { Build -> Break -> Learn -> Repeat }
I am a Master of Computer Applications (MCA) student with a strong interest in data analytics, software development, and technology-driven problem solving. I enjoy learning new technologies, building practical solutions, and continuously improving my technical skills through hands-on projects and real-world applications. My approach to learning is centered around research, experimentation, and implementation. I believe the best way to understand a concept is to apply it by building solutions that address real problems. This mindset has helped me develop experience with technologies such as Python, SQL, Flask, MongoDB, Git, and data analytics tools. I am particularly interested in creating systems that are practical, scalable, and user-focused. I value continuous learning, collaboration, and professional growth, and I am always looking for opportunities to expand my knowledge and contribute to meaningful projects. My goal is to build a career where I can combine technical expertise, analytical thinking, and problem-solving skills to create impactful solutions while continuing to grow as a technology professional.
Performed customer segmentation using clustering and classification techniques. Trained supervised ML models (Random Forest, XGBoost, SVM) and deployed an interactive Streamlit app.
Studied computer science fundamentals, data structures, algorithms, object-oriented programming, and database management systems.
Participated in product engineering and feature design within active application modules. Performed functional & integration testing to verify application processes and backend releases.
Focusing on AI/ML systems, software engineering, intelligent applications, and advanced computing.
Architected a full-stack water tanker management platform supporting four user roles (Customer, Supplier, Driver, Admin). Integrated dual ESP32 sensor nodes for real-time water telemetry and automated booking.
Interactive analytics application that predicts expected T20 scores based on historical venue conditions, career trends, and opponent match-ups using machine learning regression models deployed with Streamlit.
Centralized AI governance gateway routing organizational requests across OpenAI, Claude, and Gemini with PII masking, forbidden-keyword prompt blocking, multi-tenant isolation, dynamic USD budgeting, and sliding-window rate limiting.
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