I. Introduction
The extraction of productive information from raw data is known as data mining. Machine Learning (ML) algorithms create a numerical portrayal dependent on example information, known as “training data” to settle on different forecasts or choices without being unequivocally customized to perform the task. It extracts valuable information hidden in a wide range of enormous data. It is defined as analysing data and pattern finding and detecting regularities in vast volumes of data using soft computing techniques. To do the data mining task, we need to apply machine learning algorithms. The computer plays a vital role in finding patterns and identifying the underlying rules and features from the enormous volumes of data. The traditional way of analysing data by hand is time-consuming, error-prone; doing this, we may miss a few essential data, and large databases cannot be done by hand. Traditional statistical methods face various issues such as handling categorical data, handling missing values, and handling significant data points. To address these problems, automatic techniques are designed to analyze the data and bring out interesting patterns and other helpful information.