Blog Post about Data Science

Instead of assuming oil explains everything, I used FRED data to test where it's actually statistically significant — across inflation, unemployment, payrolls, industrial production, and GDP. The results were more nuanced than the headlines suggest.

CRISP-DM is the closest thing data analytics has to a universal playbook. Here's what the six phases actually mean in practice — and why I keep coming back to it.

Both entropy and Gini impurity measure how mixed a node is in a decision tree — but they differ in computation, behavior, and when each makes sense to use.

Both Lasso and Ridge add a penalty to linear models to prevent overfitting — but they behave very differently. Here's when to use each and how to implement both.

The Titanic dataset is the classic ML classification benchmark. Here's a full walkthrough — feature engineering, Random Forest, Logistic Regression, and what the results actually mean.

Regression predicts a number. Classification predicts a category. The difference sounds simple, but it shapes everything — which algorithms to use, how to evaluate results, and what your model actually outputs.