Classification

Classification is used to separate data into predefined categories. Based on the information provided, the model learns to identify patterns that help decide to which group the item belongs.

For example, imagine you work at a bank and want to predict whether a customer is "trustworthy" or "high risk" for receiving a loan. The model can use information such as age, income, payment history, and profession to determine which category the customer fits into.

Once trained, the classification model helps make faster and more accurate decisions, reducing errors and optimizing processes. This technique is widely used in various fields, such as predicting diseases in patients, identifying spam in emails, and even predicting the outcome of sports matches.

The classification methods include:

• Decision Tree: divides the data by simple rules in a tree structure until the final class is determined.

• KNN: classifies based on the classes of the "k" nearest neighbors of a data point in space.

• Naive Bayes: classifies using probabilities based on Bayes' Theorem and the assumption of independence between the variables.