<p><strong>Predicting Coffee Quality</strong></p><p>This workflow demonstrates how to implement a beginner-friendly machine learning approach to predict the quality of coffee beans based on various features we have about the beans, including quality measures (e.g., aroma, flavor, acidity, ...), bean characteristics (e.g., processing method, color, ...), and information about the farm (e.g., country of origin, owner, mill, ...).</p><p>In this example we implement and evaluate a <strong>Decision Tree </strong>and a <strong>Random Forest</strong> and eventually compare its performance with each other.</p><p><strong><em>Data:</em></strong> https://github.com/jldbc/coffee-quality-database/tree/master</p><p></p><p><strong>Coffee sensory attributes</strong></p><ul><li><p><em>Aroma:</em> Refers to the scent or fragrance of the coffee.</p></li><li><p><em>Flavor:</em> The flavor of coffee is evaluated based on the taste, including any sweetness, bitterness, acidity, and other flavor notes.</p></li><li><p><em>Aftertaste:</em> Refers to the lingering taste that remains in the mouth after swallowing the coffee.</p></li><li><p><em>Acidity:</em> Acidity in coffee refers to the brightness or liveliness of the taste.</p></li><li><p><em>Body:</em> The body of coffee refers to the thickness or viscosity of the coffee in the mouth.</p></li><li><p><em>Balance:</em> Balance refers to how well the different flavor components of the coffee work together.</p></li><li><p><em>Uniformity:</em> Uniformity refers to the consistency of the coffee from cup to cup.</p></li><li><p><em>Clean Cup:</em> A clean cup refers to a coffee that is free of any off-flavors or defects, such as sourness, mustiness, or staleness.</p></li><li><p><em>Sweetness:</em> It can be described as caramel-like, fruity, or floral, and is a desirable quality in coffee.</p></li></ul><p><em>Source: https://www.kaggle.com/datasets/fatihb/coffee-quality-data-cqi?resource=download</em></p>