Varun Gurupurandar

Proficient in Python for data analysis, automation, and machine learning. Skilled with:

• Pandas – Data wrangling, cleaning, feature engineering.
• NumPy – High-performance matrix and numerical operations.
• Scikit-learn – Classification, regression, and clustering (SVM, Random Forest, K-Means).
• PyTorch – Deep learning, transfer learning, and neural network training.
• Keras & TensorFlow – CNN, RNN, LSTM model development.
• Matplotlib & Seaborn – Visualization and exploratory data analysis.
• NLTK – Text processing, tokenization, sentiment analysis.
• Streamlit – Interactive ML web apps and dashboards.

Strong command over relational database systems and data querying techniques:

• RDBMS – Database design, normalization, and integrity constraints.
• Joins – INNER, LEFT, RIGHT, and FULL joins for data merging.
• Table Creation – Schema design and defining key relationships.
• Constraints – PRIMARY, FOREIGN, UNIQUE, CHECK for data validation.
• Aggregation – COUNT, AVG, SUM, GROUP BY, HAVING for analytics.
• Subqueries – Writing nested and correlated queries for complex logic.
• Performance – Query optimization and indexing strategies.

Experienced in containerization and deployment of ML and web applications:

• Virtualization – Creating isolated development environments.
• Docker Images – Writing Dockerfiles and managing layers.
• Containers – Running and orchestrating containerized services.
• Versioning – Managing image tags and rollbacks.
• Docker Compose – Handling multi-container app orchestration.
• Integration – Packaging Flask/ML APIs for CI/CD deployment.

Comprehensive experience in distributed version control and collaborative workflows:

• Git – Branching, merging, stashing, and conflict resolution.
• GitHub – Repository management, pull requests, and open-source contributions.
• GitLab – CI/CD setup and enterprise workflow automation.
• Version Management – Tagging releases and maintaining changelogs.
• Collaboration – Code reviews, issues, and workflow standardization.