One Notebook at a Time

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From Insight to Action: The Feedback Loop Every Business Needs

A one-star review at midnight costs you more than a one-star review at noon. The gap in between is the real problem. Here's what I learned building two projects to close it.

The Network That Learns Who Comes Back

It's been a minute since I last posted here — most of that gap was spent heads-down on a project I just wrapped up, so let's catch up. Where a heartbeat becomes a data point — teaching a neural network to see risk before it happens. If you've followed along before, you know this space usually lives in supervised learning — regression, classification, the more "classical" side of the toolkit. This post is a deliberate left turn into neural networks , the first one I've built and documented fully end-to-end. I expect this thread to keep rotating: NLP is probably next, and I'll likely circle back to supervised or unsupervised learning again after that. Think of it as this notebook's current chapter, not a permanent pivot. The setup The project: predict whether a patient will be readmitted to hospital within 30 days of discharge, using a 5,000-row dataset of vitals, prior admission history, chronic conditions, and a handful of administrative fields. It...

The Line of Best Fit Isn't "Best" — It's a Decision What I wish someone had told me before I ran my first regression model.

Same data. Different definition of "best." Different line." Here's something that took me longer than I'd like to admit to figure out about linear regression. That line through your scatter plot — the one everyone calls the "line of best fit" — isn't objectively the best anything. It's the best fit for one very specific definition of the word "best." Swap that definition, and you get a completely different line. Equally valid mathematically. Just built on a different set of assumptions. Once I understood that, I started looking at every machine learning model differently. What Everyone Gets Taught (And What Gets Left Out) The standard introduction to linear regression goes something like this: draw a line through your data that minimises the total error between your predictions and the actual values. Square those errors, add them up, minimise the sum. Done. This is Ordinary Least Squares (OLS) — the engine behind most introductory r...

The First Notebook

Somewhere between where I was and where I am going! Welcome to One Notebook at a Time. Every data project starts the same way. A blank notebook. A first cell. No idea yet what the data is going to say, or whether the model is going to work, or whether the whole approach is wrong and you'll scrap it by Tuesday. You just start. This blog is that first cell. Why "One Notebook at a Time" I picked this name because it's literally true on two levels. If you're a data person, you already know what a notebook is — Jupyter, Colab, whatever your tool of choice. It's where the real thinking happens. Not the polished dashboard, not the final slide deck — the notebook. Messy cells, half-finished thoughts, a graph that didn't work followed by one that finally did. But it's also true in the older sense of the word. A notebook is just where you write things down before you know what they're going to become. That's what this space is for. One notebook — one pos...