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There is an explosive growth of data due to advancements in computer methods. With ML techniques, working on a really massive quantity of data is a big problem. As a result, handling and computing on a very vast, varied, and diverse dataset seems a difficult undertaking. The purpose of this study is to provide a quick overview of several dimensionality reduction/feature selection techniques. A summary of the contributions of scholars to the development of feature selection methods for huge datasets is provided. This study is driven to create a hybrid, resilient, adjustable, as well as dynamical feature selection approach to classify huge datasets by examining current challenges.