Classification is a data analysis method used to profile large datasets. Data classification aims to establish a system for organizing data into meaningful categories. Data mining relies heavily on classification because it enables the proper classification and organization of data sets according to predetermin ed categories and ideas ( Ge et al., 2017) . A phenomenon, for instance, may be classified somewhere in the middle, between "risky" and "safe." This classification level is critical for identifying large data clusters' significance, membership, and occurrence patterns. Machin e learning uses two main processes: training and classification to classify data. In the training phase, data sets are used to teach computers to recognize the patterns that designate certain pieces of data belonging to a particular category. The foundatio n of this knowledge is the acquisition of categorization rules. This form of learning uses provided training sets to aid models in developing categorization schemes. Extensive testing is also used to make sure the model is accurate enough. As soon as the m odel finds every rule, real data is available for examination. This stage, known as classification, involves sorting actual data into meaningful categories ( Bakhshinategh et al., 2018) . Applications of classification can be found in many areas, including meteorology, communications, t he educational system, the detection of fraud, and the study of consumer preferences in the marketplace.

An association is used to understand better how various data points in a databas e are connected . To mak e sense of these connections, individual s employ an association rule, such as X-> Y, where 'X' is the premise, and 'Y' is the conclusion. The word "X" is an antec edent, and the word "Y" is the consequent. Data mining associations can mean different things dependin g on the type of data being mined.