A classification problem has the output variable in the form of a category value; for example, red or white wines; young, adult, or old. For classification problems, there are different types of classification algorithms.

Some of the most popular ones are as follows:

• Linear classifier: Naive Bayes classifier, logistic regression, linear SVM
• Nearest neighbor
• Decision tree classifier

• Support vector machines
• Random Forest classifier
• Neural network classifiers
• Boosted trees classifier

Having listed the most popular classification algorithms, we must point out that going through each of these classifications algorithms is beyond the scope of this book. However, our main intention here is to point you in the right direction. We suggest that you check out the Further reading sections of this book for more details regarding the respective topics.

A proof of concept regarding how these classifiers can be implemented in Python with the Red and White wine dataset can be found in Chapter 11, EDA on Wine Quality Data Analysis. We will discuss different evaluation techniques that can be used for classification purposes in that chapter too.