I think it’s not actually a very good ML case study for this reason. Feature engineering would be a huge part of a problem like this. Additionally, jumping to start with XGBoost is a pretty amateurish thing to do, and the very first problem to attack is class imbalance.

In the bio blurb they self-describe as an infra engineer who also enjoys data science. In some sense I really don’t like to see that. The quality of the data science / ML work in this is actually quite bad, but people use these blog posts as resume padders to try to jump into ML jobs without ever having any real experience or training.

I think it’s a bad thing because it devalues the importance of real statistical computing skills, which are very hard to develop through many years of education and experience - absolutely not the sort of thing you can get by dabbling in some Python packages on the weekend to do a little project like this.

The amount of waste I see from companies trying to avoid paying higher wages and avoid team structures that facilitate productivity of statistics experts is staggering - with all kinds of hacked up scripts and notebooks stitched together without proper backing statistical understanding, making ML engineers manage their own devops, and just ignoring base statistical questions.

For this drive problem for example, I expect to see a progression from simple models, each of which should address class imbalance as a first order concern. I expect to see how Bayesian modeling can help and how simple lifetime survivorship models can help. I expect to see a lot of feature engineering.

Instead I see an infra engineer playing around with data and trying one off the shelf framework, then claiming the whole premise can’t work in production.