I am a student at Government Model Engineering College, with a strong interest in cybersecurity and blockchain technology, complemented by experience in leveraging machine learning to solve complex problems. I enjoy taking on challenges, managing tasks efficiently, and collaborating effectively in teams or independently, with a focus on contributing to secure, innovative, and data-driven solutions.
Designed and implemented a web vulnerability scanner to identify potential security flaws in web applications. The project automated the scanning process and provided detailed reports to help mitigate risks effectively.
This project provided practical experience in cybersecurity, web application analysis, and automating security tools for effective vulnerability management.
Developed a real-time network anomaly detection system using machine learning techniques. The scanner identified abnormal traffic patterns, helping prevent potential cyberattacks.
This project enhanced my understanding of network security, anomaly detection, and machine learning for cybersecurity applications.
Developed a machine learning model for sales prediction employing neural networks, implemented in Python utilizing TensorFlow, Keras, and scikit-learn libraries. This project was created as part of the NeuralNet competition conducted during Government Model Engineering College's annual tech fest, Excel'23. The goal was to accurately predict future sales based on historical data and various features such as product attributes, time of year, and promotional activities.
Through this project, gained hands-on experience in machine learning, deep learning, and data analysis, demonstrating proficiency in Python programming and popular machine learning frameworks.