If you could confidently predict that a customer was going to leave a negative review, what would your business do differently?
Accuracy, please take a back seat. We’ll be promoting Precision and Recall today.
“Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers” — Wikipedia
For this post, let’s agree on the universal assumption that “customer churn is bad”, and “customer…
Starting out building your first multiple linear regression predictive model using Python can feel daunting! This post offers a practical workflow, guide, and example code of one approach that builds on CRISP-DM. I hope you’ll find it useful and welcome your comments.
On of the biggest benefits I've seen companies obtain using Tableau is shifting advanced data transformation and viz control to business versus IT. Business can create small transformations, semantic views, on-demand hierarchies without looping through IT. Speed to insight increases greatly, especially given the easy viz best practices. I'm glad Tableau is a leader in the "citizen data scientist" movement.
The Tableau data visualizations in this story use my personal fitness data from iPhone apps Apple Health, Peloton, and Symple. I’m a data geek and a big fan of the quantitative health movement. For over five years I have been tracking my workouts, steps, meditations, sleep, weight, net carbs, heart…