<ul><li>In today's digital banking era, financial institutions need intelligent systems to analyze customer behavior and make data-driven decisions.</li><li>To analyze customer behavior effectively, we need to process multiple types of banking data such as transactional data, account data, and customer data.</li><li>A denormalized feature table aggregates data from multiple sources into a structured format, making it easier for machine learning models and business intelligence tools to extract insights quickly.</li><li>The steps to create a denormalized feature table involve extracting data from MySQL, transforming the data for feature engineering using Python, and storing the transformed dataset back in MySQL.</li></ul>

Building a Banking Intelligence System: Transforming Customer Data for Insights

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