Telco Customer Churn Dataset
1: Introduction
Context
"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs."
Content
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
files.upload()
Saving kaggle.json to kaggle.json
{'kaggle.json': b'{"username":"hargurjeet","key":"c3882bdbb49388021171402c7018655e"}'}