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


