A COMPREHENSIVE OVERVIEW OF DATA MINING ALGORITHMS

All has access to an array of data in today's information era. Computing and storage capabilities have exploded in the last decade, and we now have the capacity to store this vast amount of data. The aim of storing and analysing this data is to extract knowledge. Classification algorithms are widely used to extract knowledge from accessible data. This paper examines a few of the most often employed data mining classification techniques. Inside a dataset, classification is used to determine which category each data instance belongs to. It's used to divide data into various categories based on those constraints. This article compares the results of the most traditional classification methods, such as Decision Tree, K-Nearest Neighbor, and Apriori. In general, classification techniques use one of three approaches: statistical, machine learning, or neural networks. While contemplating these methods, this paper offers a comprehensive overview of various classification algorithms, as well as their benefits and drawbacks.