In machine learning, a dataset is essentially a collection of data bits that may be processed as a single unit by a computer for analytic and prediction purposes. This means that the data gathered should be homogeneous and understandable to a machine, which does not see data in the same manner that people do.
Sets of numerical data.
Data sets with two variables.
Datasets with multiple variables.
Sets of categorical data.
Data sets containing correlations.
Data sets can store information such as medical records or insurance records for usage by a system application. Data sets can also be used to hold information required by applications or the operating system, such as source code, macro libraries, or system variables or parameters.
We require a large amount of data to work on machine learning projects because ML/AI models cannot be trained without data. One of the most important aspects of developing an ML/AI project is gathering and preparing the dataset.