The key to succeeding in machine learning or becoming a great data specialist is to follow different types of data sets, but finding the correct data set for any machine learning project is a daunting task. It gives detailed information about the source that can get easily obtained according to your project.
What is a dataset?
A data set is a data set in which data gets arranged in a specific order. The data set can carry any information from an array to a database table. A tabular data set gets considered as a table or matrix of a database. Each column corresponds to a specific variable, and every row corresponds to a field in the data set.
Types of dataset data
- Numerical data – It includes temperature, house prices and more.
- Categorical data – It includes true or false, yes or no, blue or green and more.
- Ordinal data – The data is related to categorical data but can be measured by comparison.
Why is the dataset needed?
You require a lot of data to process machine learning projects because, without data, machine learning and artificial intelligence models cannot get trained. Collecting and preparing data sets is one of the most crucial parts of creating an ML or AI project. If the data set is not well developed and preprocessed, the technology used in the machine learning project will not work as expected. When developing ML projects, developers have complete confidence in the data set.
Records can contain information used by programs running on the system, such as health or insurance records. The data set also get used to collect data required by the application or the operating system itself, such as source programs, macro archives, or custom variables or parameters. You can use datasets for machine learning.