I am a passionate Computer Science student with experience in web development, machine learning, and building impactful projects. I'm a full stack developer with a passion for Java, react and solving complex algorithms. I believe that technology will go far with the introduction of artificial inteligence inspiring me to create more machinne learning projects that can change the future.
This project explores the application of machine learning techniques to detect SQL injection (SQLi) attacks, one of the most common and severe web application vulnerabilities. A large dataset of over 100,000 labeled SQL queries was processed and transformed using TF-IDF vectorization to extract meaningful linguistic features. Multiple supervised classification models were trained and evaluated, including Gradient Boosting, Support Vector Machines, Decision Trees, and Logistic Regression. Through hyperparameter tuning and 5-fold cross-validation, the Gradient Boosting model achieved the highest accuracy and F1-score, demonstrating strong generalization to both traditional and adversarial SQLi patterns. The project highlights how data-driven approaches can significantly enhance web security by learning to identify malicious behavior beyond static signatures or heuristics. The implementation was done entirely in Python using Scikit-learn and pandas.
View Project Jupyter NotebookA web app to track and reduce carbon emissions. Built using ExpressJS and MongoDB.
View Project Live DemoA recipe book to search and save recipes using SpoonacularAPI using ReactJS.
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View Project Live Demo