Gaurav Mittal
Gaurav Mittal is an accomplished author and international speaker, recognized for his published articles. He has spoken at global conferences and served on several judging panels. In addition to his professional achievements, Gaurav actively contributes to non-profit organizations through volunteer work. Outside of work, he enjoys spending time with his children and playing sports.
Detailed Biography
Gaurav Mittal is an accomplished author and international speaker, recognized for his published articles. He has spoken at global conferences and served on several judging panels. In addition to his professional achievements, Gaurav actively contributes to non-profit organizations through volunteer work. Outside of work, he enjoys spending time with his children and playing sports.
Articles Authored
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Fixing Security Issues in Seconds, Not Sprints, Powered by GenAI
Last updated: Tuesday, November 11, 2025
Published in: CODE Magazine: 2025 - Nov/Dec
Gaurav Mittal writes about SecureCodeAgent, a GenAI-powered approach (using Azure OpenAI) that shifts security left by delivering real-time, in-editor code scans that identify vulnerabilities, suggest fixes, assign severity, and integrate into pre-commit/pre-push and CI workflows—reducing cost, context-switching, and post-deployment remediation compared with traditional static analyzers while improving developer education and faster, safer delivery.
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Managing Diverse Data Types in a Dataset with COLUMNTRANSFER
Last updated: Monday, March 3, 2025
Published in: CODE Magazine: 2025 - Mar/Apr
Gaurav Mittal discusses efficient data preprocessing techniques for mixed-feature datasets, emphasizing the use of the `ColumnTransformer` from the SKLEARN.COMPOSE module. By showcasing a practical application with a dataset containing numerical, categorical, and unstructured text data, he demonstrates how the `ColumnTransformer` can streamline preprocessing tasks, reducing complexity and error potential. Key transformations include scaling numerical features, one-hot encoding categorical data, and vectorizing text. Gaurav elaborates on critical parameters like transformers and remainder, highlighting their role in ensuring each feature type undergoes suitable preprocessing, ultimately optimizing machine learning model performance.

