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🚀 Customer Churn Analysis using Python
Built an end-to-end customer churn prediction project using Python, Pandas, NumPy, Matplotlib, and Scikit-learn. Performed data cleaning, exploratory data analysis, predictive modeling (Logistic Regression & Random Forest), and generated business insights to help improve customer retention.
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Join 1.25M professional creatives like you
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Behind the Scenes: Building High-Performance Desktop Applications with Electron.js
Electron has become one of my favorite technologies for creating powerful cross-platform desktop applications. It combines modern web development with native desktop capabilities, making it possible to build sophisticated software from a single codebase.
For one of my recent projects, I engineered a desktop application capable of processing live data, monitoring user interactions, and managing complex workflows in real time. Performance was a top priority, so every component from rendering to background processes was optimized to reduce memory usage and improve responsiveness.
Instead of focusing only on functionality, I prioritize clean architecture, reusable components, and maintainable code. This approach allows applications to scale while remaining easy to update as business requirements evolve.
Whether it's automation software, analytics dashboards, or enterprise desktop tools, I believe great software should feel fast, intuitive, and reliable. That's the standard I bring to every project I build.
This is my first Excel dashboard on my favorite thing ever, chocolate chip cookies 🍪
Currently building autonomous AI systems that actually survive production — not just the demo.
Most AI projects work beautifully in a controlled test and then quietly fall apart on real data, edge cases, and concurrency. I focus on the unglamorous engineering that closes that gap: data validation pipelines, boundary checks, error handling, and knowing exactly where deterministic logic should own the decision instead of the model.
Recent work includes agentic workflows built with Python and FastAPI, an LLM reasoning layer for the genuinely ambiguous decisions, and automation orchestrated through Make with MCP. My background is technical support and operations, which means I design for the failure case first — I've spent years watching systems break in ways their builders never anticipated.
If you have an AI pilot that impressed everyone in the demo and then stalled on the way to something dependable, that gap is exactly what I build for.
Open to Applied AI / AI automation projects.
Keep it up!