I am a student at Government Model Engineering College interested in machine learning and backend development. With experience in building data-driven systems, I focus on creating efficient applications that solve real-world problems. I am comfortable working both independently and collaboratively. My goal is to develop impactful data-driven solutions that enhance performance and efficiency.
A Chrome extension that leverages machine learning to analyze text and detect AI-generated content. It provides a probability score and key insights using TF-IDF, SVD, and SHAP explainability techniques.
A machine learning-based model for detecting abnormal network traffic patterns. It identifies DDoS attacks, unauthorized access, and unusual data transfers, aiding in cybersecurity threat detection through anomaly analysis.
A security analysis tool that automates the detection of web application vulnerabilities like XSS, SQL injection, and insecure headers. It leverages Python-based frameworks to perform in-depth scans and generate actionable security reports.
Developed a neural network-based sales prediction model using TensorFlow and Keras. This project was build as a part of the NeuralNet competition at Excel'23.