Abstract:
An important turning point in the field of machine learning has been reached with the convergence of data
preparation and automated machine learning (AutoML). AutoML has become a reliable solution for tackling major issues
with data preprocessing approaches because of its capacity to automate the coordination of different machine learning
processes. This study covers a wide range of important topics related to data preparation, including feature selection, timeseries
preprocessing, manual encoding mistakes, class imbalance, and inefficient hyperparameters. AutoML's revolutionary
effect on simplifying crucial data preparation procedures is one of its main contributions to data preprocessing. Data
preparation has historically been a labor-and time-intensive procedure that calls for specialised knowledge and physical
involvement at different points in the process. But many of these jobs may now be completed automatically because to the
development of automated algorithms, which has significantly increased productivity and efficiency. Furthermore, by making
data preprocessing more approachable for both specialists and non-experts, AutoML has democratised the field. Through the
automation of intricate processes like feature selection and hyperparameter tweaking, AutoML technologies enable users to
concentrate on more advanced parts of model creation, such formulating problems and interpreting outcomes. In addition to
quickening the rate of invention, this democratisation of data preprocessing encourages increased cooperation and knowledge
exchange within the machine learning community.
