Designed and developed a hybrid Internal Intrusion Detection and Protection System using Django, Python, and PostgreSQL to detect insider threats through session-based system call mining and behavioral profiling.

Engineered anomaly detection models using TF-IDF vectorization and supervised learning algorithms including Random Forest, SVM, and Naive Bayes , achieving 98% classification accuracy on API call datasets.

Integrated the trained Random Forest model into a Django web application for real-time file upload scanning, automated alert generation, and secure intrusion logging.

Developed an email spam detection system using Python and the Multinomial Naive Bayes machine learning algorithm to classify messages as spam or legitimate.

Processed and transformed text data using CountVectorizer and evaluated model performance using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC .

Built a user-friendly web interface using HTML, CSS, and Flask, with Matplotlib and Seaborn for data visualization and analysis.

Developed a full-stack event management web application using React.js, Node.js, Express.js, HTML, CSS, and JavaScript for event planning and ticket booking.

Implemented attendee registration, real-time event updates, event recommendations, and backend workflows for efficient event management.

Designed a responsive and user-friendly interface with scalable backend integration to provide a seamless cross-device experience.