Linear Regression is an extremely popular and useful model. It's used by Excel Gurus and Data Scientists alike - but how can we fit lots of regression models quickly? This article walks through various ways to fit a linear regression and speed things up with some Linear Algebra.

ReadBuilding classification models on data that has largely imbalanced classes can be difficult. Using techniques such as oversampling, undersampling, resampling combinations, and custom filtering can improve accuracy.

ReadA/B Testing can be extremely useful during experimentation. Adding statistical rigor to situations where you compare one option against another. This is one step which can help guard against making faulty conclusions.

ReadUnstructured text data requires unique steps to preprocess in order to prepare it for machine learning. This article walks through some of those steps including tokenization, stopwords, removing punctuation, lemmatization, stemming, and vectorization.

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