Preprocessing data for churn analysis

Getting data ready for churn prediction is a crucial step. It involves cleaning and organizing raw data so it's ready for analysis. Data scientists or data engineers usually handle this task.

Key steps in data preprocessing include:

• Cleaning data: Fix errors, remove duplicates, and fill in missing information
• Scaling numbers: Adjust numerical values to a common scale
• Converting categories: Change text categories into numbers
• Balancing data: Ensure fair representation of churned and non-churned users
• Creating time features: Make new data points based on historical patterns^[1]

For instance, an e-commerce team might turn purchase dates into "days since last purchase" or change product types into number codes.