Quiz: Customer Churn Analysis Using PySpark
Explore how to analyze customer churn using PySpark. Learn to load and preprocess Telco data, filter and group it to uncover churn patterns, and perform exploratory data analysis on contract types, tenure, and payment methods. This lesson equips you with practical skills to prepare and analyze big data for churn insights.
Task 1: Load the Telco churn data into a PySpark DataFrame
To begin our analysis, we’ll load the Telco churn data into a PySpark DataFrame. This task consists of two subtasks:
1.1 Load the customer data into a PySpark DataFrame
We’ll first load the customer data from a suitable data source into a PySpark DataFrame using PySpark’s built-in capabilities.
1.2 Ensure the data is properly formatted and structured for analysis
Once we have loaded the data, it’s essential to ensure that the data is properly formatted and structured for analysis by printing the first five rows and the schema of the DataFrame.