Glossary · Phase 3 · Machine learning
Tabular data
Data in rows and columns, like a spreadsheet: one row per customer, one column per fact about them.
Hand-made features matter most for tabular data. That means rows and columns, like a spreadsheet or a database table. Most business data looks like this.
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A feature must exist at the moment you make the prediction.
Learn it properly
Feature engineering · Turn raw data into signals a model can use. About 30 minutes.
Other terms from the same lesson
- feature engineering: Turning raw data into inputs that make a pattern easier to learn, like days since last purchase.
- lifetime value: The total a customer has spent with a business so far, or is expected to spend over the whole relationship.
- long tail: A shape of data with a big crowd of small values and a thin trail of a few giant ones, such as customer spending.
- quintile: One of five equal-sized groups you get by sorting values and cutting the line into fifths.
- RFM: Recency, frequency, monetary: scoring customers on how recently they bought, how often, and how much they spend. A classic marketing feature set.
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