This is part two of the multi part series where we will build a Modelshop model from scratch. You can find the first part, here. In this video, we will build on the exploratory analysis we performed last time and use machine learning to determine the important features, weights, and cut offs for optimally predicting churn. We will also learn how to deploy that ML model and extract predictions along with confidence levels. Best of all we won’t have to write any code!
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The Member Decision Optimization Tool: Better Decision-Making for Credit Unions
Credit unions face increasing pressure to make informed, consistent decisions about their members to remain competitive. Today, optimizing member interactions