A collection of blog posts about python
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.
Grid Search exhaustively evaluates every combination in a parameter grid using cross-validation. Here's how it works, when to use it, and a complete implementation with Scikit-Learn.
When your parameter space is too large for Grid Search, Randomized Search samples combinations efficiently. Here's how it works and when it outperforms the exhaustive alternative.
A hands-on tour of the Finance Toolkit's Ratios Module in Python, using real output for Apple, Amazon, Meta, and Walmart to compare profitability, liquidity, efficiency, and valuation.
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.
Main effects tell you what each variable does in isolation. Interaction effects tell you whether the combination matters. Here's how to test for both using Python's statsmodels.
Analytics engineer turning fragmented tidbits into confidence: messy data structured, patterns caught early, answers made plain with AI.
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