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Predict Churn for a Telecom Company using Logistic Regression Classification in Python & R

In this project, we are going to predict the churn customer of telecom sector and find out the key drivers that lead to churn. We are going to show how the logistic regression model using R can be used to identify the customer churn in telecome dataset.
Event Date
24th
Nov - 2017
05:30pm - 10:30pm PST
What are the prerequisites for this project?
  • This project will be done in both Python and R
  • This project assumes that you have a good knowledge of Data Science and the R language. If not - we recommend you to take the Data Science in R Programming course first.

What will you learn

  • Understand the customer behavior
  • Understand reasons for churn
  • What are the top factors
  • How to retain customers
  • Apply multiple classification models

Project Description

Customer churn refers to a decision made by the customer about ending the business relationship. It is also referred to the loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from the business relationship and the factors affecting the customer decisions. Here we are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

Instructors

 

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