Detecting Coffee Outliers with Near IR and Python

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In this video, I demonstrate how to use Principal Component Analysis (PCA) to detect potential chemical outliers in Near-Infrared Spectroscopy (NIRS) data from coffee samples. Watch as I build a PCA model using part of the dataset and then apply it to a second set of data to uncover hidden patterns and outliers. If you're interested in NIRS data analysis or PCA modeling, this video will provide practical insights. Check it out to see the results!

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