About
Computer Science and Engineering graduate (B.E., CGPA 8.1) with a strong foundation in Python, Object Oriented Programming, Data Structures and Algorithms, and SQL. Built a machine learning fraud detection system in Python and completed a full-stack development internship covering REST-style backend services.
Projects1
UPI Fraud Detection Using Machine Learning
Developed a Python-based machine learning system to detect fraudulent UPI transactions using supervised learning models: Logistic Regression, Random Forest, and K-Nearest Neighbors. Performed data processing and feature engineering on transactional and behavioral data to improve fraud detection accuracy. Applied SMOTE to handle class imbalance in the dataset. Evaluated models using Precision, Recall, F1-Score, and ROC-AUC, prioritizing low false negatives to minimize financial risk.
- Python
- Logistic Regression
- Random Forest
- K-Nearest Neighbors
- SMOTE
Education1
Bachelor of Engineering
2022 — 2026JNN College of Engineering (JNNCE)
Computer Science and Engineering
Certifications1
Artificial Intelligence A-Z 2025: Agentic AI, Gen AI and RL
2025Udemy