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Telco Customer Churn Dataset

1: Introduction


"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs."


Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

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

2.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

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

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

2: Data preprocessing

2.1 Downloading the Dataset from Kaggle

from google.colab import files
Saving kaggle.json to kaggle.json
{'kaggle.json': b'{"username":"hargurjeet","key":"c3882bdbb49388021171402c7018655e"}'}