Machine Learning

When you’re working with time series data, naturally things are going to point towards forecasting. And two of the most reliable classical tools for this are the ARIMA and SARIMA models. To be honest I was quite confused when I first learnt about these a year ago but they do make a ton of intuitive sense once you understand them on a deeper level. The names are similar, and so is their underlying logic, but there is a key difference that more or less decides which one you will pick in what scenario. ...

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. ...

Deep Learning Gets the Spotlight, But Time Series Still Solves Real Problems In the machine learning landscape today, deep learning models - transformers, LSTMs, and other neural networks steal the show. They’re impressive, powerful, celebrated and make you feel smart too when you use them. However, when it comes to forecasting business metrics like sales, demand, or inventory, deep learning isn’t always the answer. Traditional time series models, especially ARIMA (AutoRegressive Integrated Moving Average) and its seasonal extension SARIMA, are some of the most effective and interpretable methods for forecasting structured temporal data. ...

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. ...