Churn Analysis

Overview

The leading telecom company has a massive market share but one big problem: several rivals that are constantly trying to steal customers. Because this company has been the market leader for so many years, there are not significant opportunities to grow with new customers. Instead, company executives have decided to focus on their churn: the rate at which they lose customers. They have two teams especially interested in this data: the marketing team and the customer service team. Each team has its own reason for wanting the analysis. The marketing team wants to find out who the most likely people to churn are and create content that suits their interests. The customer service team would like to proactively reach out to customers who are about to churn, and try to encourage them to stay. They decide to hire you for two tasks: 1. Help them identify the types of customers who churn 2. Predict who of their current customers will churn next month. To do this, they offer you a file of 7,000 customers. Each row is a customer. The Churn column will say Yes if the customer churned in the past month. The data also offers demographic data and data on the services that each customer purchases. Finally there is information on the payments those customers make. Open-source project by Alexis Linxsly, published on GitHub.

Highlights

• 1 star on GitHub
• Primary language: Jupyter
• Open source — view the code and contribute on GitHub

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