/Customer-Churn-IBM-dataset-

Use R and Python to create preliminary visualizations of key data aspects, Wrangle data to prepare for single sample t-test for means and multiple linear regression, then create professional visualizations in Tableau.

Primary LanguageJupyter Notebook

Customer-Churn-IBM-dataset-

Team Phoenix has banded together to find out what affects customer churn most, and how much money people spend when they fall into the "retained" category or "churned" category.

The team uses IBM's Telecommunications dataset with over 7 thousand rows.

First you'll see us wrangle data in Python and R. Next, we come up with some extra questions to ask which help us understand our data better. Then we use a few visualizations to illustrate that understanding

Finally, we execute the statistical analyses needed to come to our conclusions about customer Churn.