Data Scientist & Open-Source Contributor building practical AI and automation tools using Python.
I'm Sameer Shukla, a BS Life Science graduate from the University of Delhi with a Computer Science minor, working at the intersection of Data Science, Machine Learning, and open-source software.
My journey started with science and curiosity — and led me to Python, Power BI, machine learning, and AI agents. I love building tools that solve real problems and sharing them with the community.
I believe the most impactful software is open, collaborative, and built in public. I actively use, study, and contribute to open-source projects — from large ecosystems like webpack to my own public repositories.
As an open-source contributor, I focus on tools I genuinely use — submitting fixes, improving documentation, and engaging with maintainers. Every contribution, however small, is a step toward making the ecosystem better for everyone.
Exploring AI-powered automation agents for workflow automation, intelligent responses, and data-driven decision support. Integrating Python tools with modern AI frameworks and APIs to build practical productivity-enhancing applications.
A cross-platform Python TTS app using pyttsx3 with automatic fallback to native system commands (Windows PowerShell / macOS say). Supports interactive CLI and one-shot argument mode with a full pytest test suite.
Automates attendance tracking via real-time webcam facial recognition. Matches faces against stored encodings, logs name + timestamp to CSV with duplicate-prevention. Demonstrates ML-based encoding and real-time image processing.
Deep exploratory data analysis of the Netflix content library. Uncovers trends across genres, countries, release years, and content types. Combines statistical analysis with rich visualizations to drive actionable insights.
Sales forecasting model for US retail stores using time-series analysis and predictive modeling. Identifies seasonal trends and demand patterns to help businesses optimize inventory and strategy decisions.
A productivity app that schedules and tracks tasks with completion status. Explores enforcing focus by restricting device usage when tasks aren't completed on time — combining productivity management with behavioral accountability.
I contribute to the open-source tools I rely on every day — from build systems to data libraries. Here's where I've engaged with the community beyond my own projects.
The most widely used JavaScript module bundler — powering millions of projects worldwide including React, Vue, and Angular toolchains.
Contributed to the webpack core repository. Engaged with the maintainer community on one of the most critical tools in the modern JS ecosystem.
The foundational data science stack — NumPy for numerical computing, Pandas for data manipulation, and Scikit-learn for machine learning. Tools I use in every project.
Active user of these libraries across all my data science projects. Engaged with community discussions, issue tracking, and documentation improvements.
The world's most popular computer vision library. Used in my Face Attendance System for real-time webcam capture, image preprocessing, and frame analysis.
Built a real-time face recognition attendance system on top of OpenCV. Engaged with the community around Python bindings and image processing pipelines.
The go-to Python testing framework. I use pytest in my projects to write clean, maintainable test suites — including a full suite for RoboSpeaker.
Wrote comprehensive test suites using pytest for my CLI and automation projects. Followed pytest's own documentation standards and community best practices.
A powerful open-source workflow automation tool. I use n8n to build automated pipelines integrating Telegram bots, Google Sheets, Gmail, and AI APIs.
Built production automation workflows for inventory management and order tracking systems. Actively engaged with the n8n community for workflow design patterns.
The container platform that enables consistent, reproducible development environments. An essential tool in my infrastructure and deployment workflow.
Used Docker to containerise Python applications and ensure consistent environments across development and deployment pipelines.
Open to open-source collaborations, internships, and data science opportunities.