Telecom Customer Churn Prediction Project

Project overview:

This analysis focuses on the behavior of telecom customers who are more likely to leave the platform.

We intend to find out the most striking behavior of customers through EDA and later on use some of the predictive analytics techniques to determine the customers who are most likely to churn.

Define the problem:

One of the most common problems at work is turnover.

Replacing a worker earning about 50,000 dollars cost the company about 10,000 dollars or 20% of that worker’s yearly income according to the Center of American Progress.

Replacing a high-level employee can cost multiple of that...

Cost include:

Cost of off-boarding

Cost of hiring (advertising, interviewing, hiring)

Cost of onboarding a new person (training, management time)

Lost productivity (a new person may take 1-2 years to reach the productivity of an existing person)

Example

• Jobs (earning under 30k a year): the cost to replace a 10/hour retail employee would be 3,328 dollars.
• Jobs (earning 30k-50k a year) - the cost to replace a 40k manager would be 8,000 dollars.
• Jobs of executives (earning 100k+ a year) - the cost to replace a 100k CEO is 213,000 dollars.

The data set includes information about:

• Customers who left within the last month – the column is called Churn

• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies

• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges

• Demographic info about customers – gender, age range, and if they have partners and dependents

Methods applied:

• Exploratory Data Analysis

• Data Pre-processing

• Modeling and Evaluating

Data source:

IBM