This post introduces logistic regression as a linear model for building predictive models from observed data, specifically designed to predict binary values. It discusses how logistic regression can be used in conjunction with Redis using the Redis-ML module, which provides ML.LOGREG.SET and ML.LOGREG.PREDICT functions to create and evaluate logistic regression keys. The post demonstrates how to perform a logistic regression on the Fisher Iris Plant Data Set and uses Redis to emulate the One vs. Rest procedure for multiclass classification. It shows that using Redis can provide a highly available, real-time classifier for data, and will explore matrix operations supported by Redis-ML in the next post.