I write code that helps me think better, build faster and solve deeper. My code spans industries, langiages and toolkits β built for reuse across industries, academia and everyday life.
These are designed to work on nearly any real-world dataset β across industry and academic use cases.
- ML Toolkit β A curated collection of machine learning workflows with embedded explanations for fast, reusable implementation.
- Statistics Toolkit β Applied statistics workflows with explanatory depth, covering A/B testing, hypothesis testing, and introductory statistics.
- DataFrame Explorer β R package to understand dataframes, reducing data coding time by facilitating familiarity before manipulation.
- tech-tributes β Creative writeups that reframe iconic tech stories through data/code.
- times-of-twitter β Data-driven experiments with social media and timelines.
- bitcoin-utils β A playground for analyzing crypto patterns and building lightweight tooling.
- zero-digital-footprint β A practical guide for protecting your privacy and managing your digital footprint.
- digital-homestead-guide β Markdown-based guides for privacy-conscious system setup, software choices and local-first workflows.
- Languages β Python, SQL, R
- Libraries β Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch
- Visualization β Plotly, Matplotlib, GGplot2, Looker, Tableau, R-Shiny
- Big Data & Infra β Apache Spark, Hive, Hadoop, Databricks, AWS Redshift
- Cloud β Google Cloud, AWS, Airflow
Code is how I think. These repos reflect not just what Iβve built β but how I approach problems, stay sharp and document ideas that matter.