Machine Learning

Python

Time Series Exploratory Data Analysis

Exploring time series autocorrelations and change points can help familiarize with potential breakdowns in approaches. In this article, I review testing for stationarity, seasonal decomposition, autocorrelation, and change point detection.

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Python

Speed up Linear Regression with Matrix Math

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.

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Python

Classification with Imbalanced Data

Building 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.

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Python

A Straightforward Guide to A/B Testing

A/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.

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