☕ Clustering of Coffee quality

🔗 Link - (https://www.kaggle.com/code/arkabarua173/clustering-of-coffee-quality-k-means-pca)

📌 Objective:

• Clustering of coffee quality with PCA and K-means

📄 Dataset Description:

• REC_ID: Refers to the unique database ID.
• Species: Refers to the botanical species of the coffee beans, such as Arabica or Robusta.
• CreditScore: The credit score of each customer. Continuous Variable.
• Continent.of.Origin: Refers to the continent of origin for the respective coffee lot record.
• Country.of.Origin: Refers to the country of origin for the respective coffee lot record.
• Harvest.Year: Refers to the year harvested for the respective coffee lot record.
• Expiration: Refers to the assigned expiration date for the respective coffee lot record.
• Variety: Refers to the specific cultivar or type of coffee plant from which the beans are harvested.
• Color: Refers to the observed color of raw coffee bean. Typically blue, green, or mixed.
• Processing.Method: Describes the method used to process the coffee beans after harvesting.
• Aroma: Refers to the scent or fragrance of the coffee.
• Flavor: Evaluated based on the taste, including any sweetness, bitterness, acidity, and other flavor notes.
• Aftertaste: Refers to the lingering taste that remains in the mouth after swallowing the coffee.
• Acidity: Refers to the brightness or liveliness of the taste.
• Body: Refers to the thickness or viscosity of the coffee in the mouth
• Balance: Refers to how well the different flavor components of the coffee work together.
• Uniformity: Refers to the consistency of the coffee from cup to cup.
• Clean.Cup: Refers to a coffee that is free of any off-flavors or defects, such as sourness, mustiness, or staleness.
• Sweetness: Refers to the pallet of sweetness offered by its taste.
• Moisture: Represents the moisture content of the coffee beans, typically measured as a percentage.
• Quakers: Indicates the presence of quaker beans, which are unripe or defective beans that fail to roast properly.
• Category.One.Defects: Refers to the total number of first level defects, such as black or sour beans.
• Category.Two.Defects: Refers to the total number of second level defects, including more severe defects like moldy, insect-damaged, or perforated beans.

🔨 Data Preprocessing

• SimpleImputer with mean strategy is used fillup missing values.
• Principle Component Analysis (PCA) is used reduce the number of dimensions of the dataset. 3 components is used as they capture almost 93% of the total variance.

🤖 Model Used:

• K-means Clustering used for this experiment.
• The number of clusters is chosed using elbow method. The optimal number of clusters for this dataset is 6.