Python

If you are trying to decide between ARIMA and SARIMA, the thing you are really checking for is seasonality. ARIMA is for a series with a trend or short-term dependence, but no predictable repeating cycle. SARIMA is for when you have that same stuff plus a repeating pattern, like monthly demand going up every December or daily traffic acting differently every weekend. Pretty much. The annoying bit is that seasonality can look like random mess until you actually plot the data. Also, a seasonal model sounds more complete, so it is tempting to throw one at everything. I have done that. Forecasting gets annoying pretty fast when the model is more complicated than the data needs it to be. ...

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. ...

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. ...