Capstone Part 6: Window Feature Engineering for Analytics

How can we compute user lifetime spend, previous transaction intervals, and transaction sequence rank?

Production⏱ 3 Minutes⭐ 850 XP

🤔 THE QUESTION

How can we compute user lifetime spend, previous transaction intervals, and transaction sequence rank?

💡 WHAT IS IT?

Applying reusable Window specifications computes progressive analytical features in a single execution stage.

🎯 WHAT IS IT USED FOR?

Feature store generation for predictive machine learning models and analytical dashboards.

💻 EXAMPLE

w = Window.partitionBy("user_id").orderBy("event_timestamp")
w_cum = w.rowsBetween(Window.unboundedPreceding, Window.currentRow)
df_features = df_scored.withColumn("user_lifetime_spend", sum("amount").over(w_cum)).withColumn("tx_sequence", row_number().over(w))

🎯 Mission Objectives

Practice typing production-grade PySpark code for Capstone Part 6: Window Feature Engineering for Analytics.

✓ Lifetime spend accumulation
✓ Sequence ranking
✓ Feature store engineering

🚀 Start Mission

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