Up to this point in the thesis series, everything has been about writing — choosing a topic, drafting an outline, running the literature review. This post is a detour into tooling. I'd been using Claude through the web chat for a while, and I wanted to try Claude Code — the terminal-based version — alongside... Continue Reading →

In the last post I talked about choosing a topic and drafting an outline. This one is about what I actually did once the outline existed: the literature review. Not the idea of a literature review. The tools I opened, the order I used them in, and how I went from a blank Introduction section... Continue Reading →

I’m in the final months of my master’s, and if I don’t finish the thesis, I’m out. So this is me trying to get it done, and documenting the process as I go. My master’s program is in Data Science and Artificial Intelligence, and the first question was the one that sounds easiest but isn’t:... Continue Reading →

Getting your predictions onto the leaderboard, step by step You have built a model, generated predictions, and saved them to a CSV file. Now you need to get that file onto Kaggle and see how it scores. The process is not complicated, but it has a few specific steps that are not immediately obvious the... Continue Reading →

Part 9: The Final Model and Submission Everything up to this point has been preparation. The feature engineering produced 142 inputs. The validation run found the optimal number of boosting iterations and confirmed a validation SMAPE around 13.6%. Feature importance analysis identified which of those 142 features actually contributed. Now all of that feeds into... Continue Reading →

Part 8: Feature Importance In this section, we will explain the concept of decision tree feature importance and why it matters for understanding your model. The model has been trained. The next natural question is: out of the 142 features that went in, which ones actually drove the predictions? Tree-based models like LightGBM give a... Continue Reading →

Part 7: Training LightGBM; Gradient Boosting, Hyperparameters, and Early Stopping The feature set is ready. The validation split is in place. The next step is fitting the model, and understanding what is actually happening during that fit. LightGBM is a gradient boosted tree framework, which means the training process is iterative: it builds one tree... Continue Reading →