Data Science

When Copy‑Paste Stops Working In marketing, we live and die by templates. Landing page templates. Ad copy frameworks. Campaign recipes. Machine learning wouldn’t be that different, right? Just plug in your dataset, borrow someone’s notebook from Kaggle, tweak a few parameters, and be done with it. That’s how a lot of people approach it. And to be fair, it kind of works, until it doesn’t. You get a model that runs, a prediction that looks reasonable, a chart that impresses in a meeting. But under the surface, there’s often a gap. You don’t really know what’s going on. ...

If you read part 1 of this series, ala Churn Prediction for E-Commerce with Predictive Modelling, you know I recently wrapped up a full end-to-end churn prediction project as part of my postgrad program. That article was the 30,000-foot view – the business problem, the segmentation insights, the high-level model results. With this one I simply walk you through the code. But instead of just dumping code snippets for you to copy pasta, I want to walk you through what I actually did and, more importantly, why it matters. ...

A stakeholder comes to your desk. They’re excited. “We need to use AI,” they say, “to improve customer retention.” You nod, open your editor, and you start thinking. Should I use XGBoost? Or maybe a neural network? How will I set up the pipeline? Stop. Right there. This is the single biggest mistake many of us make when we’re starting out: we jump straight to thinking about solutions and algorithms. ...

It’s been a year in data science and machine learning. Okay, I lied. Technically a full year and a few months since I officially splooted (wanted to show off my extensive vocabulary) into the world of data science and machine learning with my master’s program. In late 2023 I started learning data science through a Udemy course and in January of 2024 I gave up. Well, not exactly per say. ...

Customer churn isn’t just a marketing problem - it’s a business survival issue. In competitive industries like e-commerce, losing one customer often means losing several revenue streams, especially when one account can represent multiple users. This post is a breakdown of my churn prediction capstone project for the postgraduate data science program at UT Austin - also tied to my master’s in data science at Deakin U. The project was closed-source, so I can’t release the full notebook, but I’ll walk you through everything I did including code snippets, results, charts, what I learned, and where this project fits in my larger journey into machine learning and MLOps. ...