Telecom churn case study python github

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From: Andrew P.
Added: 11.04.2021
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It takes an input relation with numerical columns, and calculates Pearson Correlation Coefficient between each pair of its input columns. This function is implemented as a Multi-Phase Transform function, and employs the powerful Vertica distributed execution engine to run at scale. In this blog post, we will first review the concept of a correlation matrix briefly. Then, we introduce our new function, and show you how to use it with the help of an example.
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Case Study: Churn Prediction

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Predicting Customer Churn with Python – Nolan Greenup – Data Science | Boston, MA

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Predicting Customer Churn with Python

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