Computer Science Engineering Student
Computer Science Engineering student specializing in AI & ML with hands-on training in Data Analytics. Skilled in Python, SQL, Excel, Power BI, data visualization, exploratory data analysis, and machine learning.
Ancen Digital Marketing (Startup Venture)
2023 – 2025
Executed digital marketing campaigns, social media handling, and brand promotion for clients including Radisson Hotel, Tritiksha Jewellers, and Healthy Bites. Developed content strategies, managed social media calendars, and increased online visibility through targeted engagement.
Skill Dzire Technologies
2024
Implemented machine learning algorithms achieving 85%+ accuracy; developed 3 AI applications. Completed 200+ hours of AI and deep learning training.
Skill Dzire Technologies
2025 – 2026
Completed an intensive internship focused on Python fundamentals, problem-solving, and practical coding applications.
Ancen Digital Marketing
2 yrs 11 mos · vizag
Led a team of 4, acquired enterprise clients, and generated INR 500,000+ revenue. Increased client engagement by 40% and social media reach by 65%.
Skill Dzire Technologies
4 mos
Completed a long-term internship focused on Python fundamentals, problem-solving, and practical coding applications.
Pawzz Community Initiative
3 mos
Managed fundraising for 50+ stray dogs, exceeded targets by 25%, and contributed INR 10,000+ to animal welfare programs.
B.Tech, Computer Science Engineering (AI & ML)
2022 – 2026
Intermediate, MPC
2020 – 2022
SSC / Class 10
2019 – 2020
Percentage: 55.4%
intermidiate, MPC
2020 - 2022
Percentage: 55%
Led a team to conceptualize a healthcare chatbot using Groq’s LLaMA-3.3-70B-Versatile model with a Streamlit front end and Neon PostgreSQL for data storage. Achieved 92% symptom analysis accuracy, integrated emergency alerts reducing response time by 60%, and added a computer vision monitoring module achieving 95% detection accuracy. Technologies: Streamlit, Neon PostgreSQL, Groq LLM (LLaMA-3.3-70B).
Built a YOLOv8-based computer vision system for real-time people detection with 98% accuracy. Automated entry/exit counting with 95% accuracy and optimized 30 FPS video processing. Technologies: Python, YOLOv8, OpenCV.
Built a classification model using Python and Scikit-learn with exploratory data analysis, preprocessing, and KNN, SVM, and Random Forest algorithms. Evaluated performance using accuracy, precision, recall, and F1-score.