Trust the data to speak for itself
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summary
Covers exploratory data analysis (EDA) and data visualization as essential steps before modeling, explaining how EDA (feature selection, feature engineering, and dataset understanding) guides choice of machine learning algorithms and improves model accuracy. Explains how visualization communicates results to non-technical end-users to enable data-driven decisions and discusses risks of neglecting EDA and visualization.
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