Regression

Visualising Decision Trees

I’ve seen lots on how decision trees work, but often the descriptions are textual. I’m a visual person, so let’s do a visual exploration. The code for this little project is available in my GitHub here: https://github.com/jamesdeluk/data-projects/tree/main/visualising-trees I’m not going to talk about how the splits are done mathematically (i.e. how they minimise MAE/MSE for regression models, or how they calculate Gini impurity or entropy for classification models); this is more from the use-case perspective, understanding the impact of different hyperparameters on getting the result you want.

Sunday, August 17, 2025 | 9 minutes Read

Kaggle: Predict the Introverts from the Extroverts

I’ve spent the last few months (and continue to spend them) working for an EV startup - as such, I’ve had a lot less time for personal projects such as this. The Kaggle notebook for this project can be found here: https://www.kaggle.com/code/jamesdeluk/ieclassifier It’s a quick and dirty one, not particularly tidy - I just felt the urge to have a play. Don’t judge me. Intro A simple challenge from Kaggle’s Playground series, called Predict the Introverts from the Extroverts. It can be found here: https://www.kaggle.com/competitions/playground-series-s5e7

Friday, July 25, 2025 | 7 minutes Read

Basic APIs: Diamond Pricing with FastAPI, Docker, GitHub Actions, and Azure

This has been a long time coming! Basic APIs part one was posted back in January; since then I’ve started a new job and have had some contract/freelance work, so I didn’t get around to completing this. Also, since then, I’ve moved from Windows to Mac, and started using uv instead of pyenv/pip, so things may look a little different. The repo for this project can be found here: https://github.com/jamesdeluk/data-projects/tree/main/basic-apis/diamond-price The goal Send (POST) to an online API with diamond criteria, and get a predicted price as a response.

Thursday, March 6, 2025 | 12 minutes Read

Disaster Tweets Natural Language Processing

Intro I have a dataset of tweets, which includes whether they are referring to a disaster or not. The goal is to build a model that takes a tweet and predicts if it is a disaster. This could be useful during an actual disaster to ensure only the most relevant ones are shown to emergency responders. The full code for this project can be found on my GitHub: https://github.com/jamesdeluk/data-projects/tree/main/nlp-with-disaster-tweets Exploring and cleaning the data I started by looking at the raw data in a text editor; as it was only a few hundred kilobytes, it was easy enough to do:

Wednesday, December 11, 2024 | 26 minutes Read

Customer Analysis Part IV: Brand Analytics and Elasticity

This is part four of a multi-part series. Part one, segmentation and clustering, can be found here. Part two, classification, is here. Part 3, purchase analytics, here. As before, the code below is simply snippets. The full code for this section can be found in the repo: https://github.com/jamesdeluk/data-projects/blob/main/customer-analysis/ca4_brands.ipynb Intro At last we reach out final part in this series. Here we’re looking at brands, quantities, and elasticities. Data The first thing is to reimport the data, the same as part three. I also created NumPy array of prices I’ll use later for testing and simulation. We know the prices in our dataset range from 1.10 to 2.80; for nice numbers, I picked 1.00 to 3.00, in 0.01 intervals:

Sunday, December 1, 2024 | 20 minutes Read

Customer Analysis Part II: Classification

This is part two of a multi-part series. Part one, segmentation and clustering, can be found here. Code for this section can be found in the repo: https://github.com/jamesdeluk/data-projects/blob/main/customer-analysis/ca2_classification.ipynb Intro Great, we have our customers clustered! But, hopefully, over time, we’ll gain more customers, and they’ll need to be assigned to an existing cluster. This is called classification. There are a few techniques for doing this. First, let’s remind ourselves what our current clusters look like by grouping the data and finding the means, as we did in part one:

Monday, November 18, 2024 | 19 minutes Read

How much for my car? Using machine learning to find out

Intro I own a wonderful Ford Fiesta ST-3 (2017, 7th generation, 38k miles), but since moving to London, she’s been sat at my parents’ house. I finally accepted it’s best to sell her. But how much should I ask? Let’s do some data analysis and machine learning! The JupyterLab notebook, extractor script, and data, is available on Github: https://github.com/jamesdeluk/data-projects/tree/main/car-price-regression-modelling Gather the data Auto Trader makes it difficult to scrape data - the page is loaded dynamically, and has robots.txt etc to restrict bots, so I couldn’t find an online tool that worked. They do provide an API, but that’s only for industry, and likely costs a pretty penny.

Wednesday, October 16, 2024 | 21 minutes Read