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📘 clustering-extracting-patterns

The base that I used has information on approximately 9k customers and how they use their credit cards.

I will analyze the CC GENERAL.csv file with 8950 instances, that is, almost 9k clients, and I'm gonna have columns that represent different attributes: customer id, balance (limit available on the account), frequency that the balance is changed, value for purchases in cash and in installments, among others. There are a total of 18 attributes.

🧑‍💻 What I learned:

. Discover how to validate and interpret results with data without labels.

. Learn techniques that will help you interpret cluster information.

. Extract information about customer behavior using data from a credit card company.

. Use scikit-learn to generate clusters and calculate different validation metrics.