1. Introduction

Customer Churn in TELCOs

Companies usually have a greater focus on customer acquisition and keep retention as a secondary priority. However, it can cost five times more to attract a new customer than it does to retain an existing one. Increasing customer retention rates by 5% can increase profits by 25% to 95%, according to research done by Bain & Company.

Churn is a metric that shows customers who stop doing business with a company or a particular service, also known as customer attrition. By following this metric, what most businesses could do was try to understand the reason behind churn numbers and tackle those factors, with reactive action plans

The main goal is to develop a machine learning model capable to predict customer churn based on the customer’s data available.

I'll using the A sample Teleco Churn dataset from Kaggle. The structure of this notebook is as follows:

• First, loading and viewing the dataset.
• The dataset has a mixture of both numerical and non-numerical features, that it contains values from different ranges, plus that it contains a number of missing entries.
• Preprocessing of the dataset to ensure the machine learning model we choose can make good classifications.
• After our data is in good shape, exploratory data analysis to build our intuitions.
• Finally, building a machine learning model that can predict if an individual would churn the service.

• Author - Chinmay Gaikwad
• Email - chinnmaygaikwad123@gmail.com