From Raw Data to Insights: A Practical Guide to EDA (Part 1)

The article emphasizes the critical role of exploratory data analysis (EDA) in building reliable machine learning models, particularly in banking and finance. It highlights that treating data understanding as a preliminary step is essential for identifying biases and operational constraints early on, which can prevent costly issues later. Developers should focus on thoroughly interrogating their datasets to ensure they capture the true nature of business operations before proceeding with model development.

Read the full article at Towards AI - Medium

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