
How to Select Features for Your Machine Learning Model?
Feature selection distinguishes the best data scientists from the others. Get techniques and tips for choosing the best features effectively.
For over a decade, I worked at the intersection of finance, marketing, sales, and data analysis, helping global FMCG brands make smarter, more profitable decisions. As a revenue manager, I built strategies and models that optimised pricing, streamlined financial forecasting, and uncovered growth opportunities hidden in complex datasets.
But while working with data to drive corporate success was rewarding, I started to feel the itch to build something of my own, being free from corporate bureaucracy and politics. So, I left the corporate world, and began actively learning programming, data engineering, and data science to better equip myself for a next chapter.
Now, I'm applying my experience to building SaaS products from the ground up together with my partner. Currently, we're working on:
πΈ Roadmappy β the AI-native customer intelligence and roadmapping platform for product and commercial teams.
I'm sharing our journey on X and Bluesky.
On this site, I share everything about strategy, pricing, data analysis, and data science. It's all grounded in credible sources and shaped by my own experience.
If you're into product dev, data-driven growth, and entrepreneurship in the AI era, stick around!

During my career, I have been lucky to work with 40+ global, regional and local brands. Some of them comprise:
















Feature selection distinguishes the best data scientists from the others. Get techniques and tips for choosing the best features effectively.

How to craft an effective strategy? The best answer is provided in Richard Rumelt's book 'Good Strategy / Bad Strategy'. This article explores Rumelt's core concepts and offers a few thoughts on his ideas and the book as a whole.