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Project 3 - Telecom Churn Classification Model

Overview

  1. This GitHub repository represents the final project submission for Greg Osborne's Third project at FlatIron school. The project assignment was to analyze a selected set of data for churn rate predictions to present to Telecom.

Business Understanding

  1. Telecom wants to limit their customer departure rate, or churn rate, to as low as possible.
  2. I made business recommendations based on the following three variables:
    • Oregon
    • Customer Service Calls
    • International Plan

Data Understanding and Analysis

  1. Source of data
    • All data used in this project was from Telecom. It is a database full of various statistics regarding specific customers and which left and which stayed.
  2. Visualizations One visualization (the same visualization is in the notebook)
    • Commission Seven graphics exclusively for aesthetics in the Google Slides doc.
    • OutlineVisual
    • Commission
    • Commission
    • Commission
    • Commission
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    • Commission
    • Commission
    • Questionmark

Conclusion

  • This analysis is limited by time and budget. There are many other factors to consider when trying to predict a customer's churn rate. The three recommendations I make are:
    1. Check ORegon. * Oregon had the highest churn rate of all states. * Is there greater competition in Oregon?

    2. Review Customer Service Calls * The more often customers called customer service, the more likely they were to leave. * What calls are departing customers making to customer service? * How do departing customers rate customer service in the survey results? * How does the wait time for customer service compare to other companies? * What do people say about our customer service? What about online?

    3. Review International Rates. * Departing customers were four times more likely to have an international rate plan. * Every departing customer had made at least one international call. * How does Telecom’s international plan hold up against the competition?

Repository Structure

  ├── data : data used for modeling
  ├── Visualizations : images used in PPT and readme
├──.canvas : File provided by Flatiron
├──.gitignore : File provided by Flatiron
├──github.pdf : PDF of repository
├──LICENSE.md : File provided by Flatiron
├──Notebook.pdf print of jupyter notebook used for modeling
├──Presentation.pdf : Presentation for Stakeholders
├──README.md : project information and repository structure
└──student.ipynb : Jupyter Notebook

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