Predicting Credit Card Sales: A Comprehensive Guide to Quarterly Forecasting

Understanding the Key Components of Credit Card Sales Time Series

Credit card sales data typically exhibits several important patterns that must be accounted for in your forecasting model:

1. Trend Component: The long-term direction of sales (growth or decline)
2. Seasonal Components:
   • Weekly Seasonality: Different spending patterns by day of week
   • Monthly Seasonality: Monthly payment cycles and salary effects
   • Quarterly Seasonality: Business cycles and quarterly reporting effects
   • Annual Seasonality: Yearly patterns like holiday shopping seasons
3. Holiday Effects: Specific spikes or drops around holidays
4. Special Events: Sales, promotions, or other company-specific events

Data Preparation and Exploration

Before building your model, you should:

Automatically Load Code in Jupyter Notebooks Using the IPython Startup Folder

If you frequently work with Jupyter notebooks, you’ve probably found yourself writing the same import statements, utility functions, or configuration settings at the beginning of each notebook. This repetitive task can be eliminated by taking advantage of IPython’s startup folder functionality, which allows you to automatically load code every time you start a new notebook.

In this guide, we’ll explore how to set up and use the IPython startup folder in both Windows and Unix-based systems (macOS/Linux).

Introduction to DuckDB

DuckDB has emerged as a game-changer in the data processing landscape. It’s an in-process SQL OLAP database management system, similar to SQLite but optimized for analytical queries rather than transactional workloads. What makes DuckDB particularly attractive is its remarkable performance for data analysis tasks combined with its simplicity - no server setup, no complex configuration, just a library you can import into your Python scripts or notebooks.