Features of a Dataset
The features of a dataset may allude to the columns available in the dataset. The features of a dataset are the most critical aspect of the dataset, as based on the features of each available data point, will there be any possibility of deploying models to find the output to predict the features of any new data point that may be added to the dataset.
It is only possible to determine the standard features from some datasets since their functionalities and data would be completely different when compared to other datasets. Some possible features of a dataset are:
- Numerical Features: These may include numerical values such as height, weight, and so on. These may be continuous over an interval, or discrete variables.
- Categorical Features: These include multiple classes/ categories, such as gender, colour, and so on.
- Metadata: Includes a general description of a dataset. Generally in very large datasets, having an idea/ description of the dataset when it’s transferred to a new developer will save a lot of time and improve efficiency.
- Size of the Data: It refers to the number of entries and features it contains in the file containing the Dataset.
- Formatting of Data: The datasets available online are available in several formats. Some of them are JSON (JavaScript Object Notation), CSV (Comma Separated Value), XML (eXtensible Markup Language), DataFrame, and Excel Files (xlsx or xlsm). For particularly large datasets, especially involving images for disease detection, while downloading the files from the internet, it comes in zip files which will be needed to extract in the system to individual components.
- Target Variable: It is the feature whose values/attributes are referred to to get outputs from the other features with machine learning techniques.
- Data Entries: These refer to the individual values of data present in the Dataset. They play a huge role in data analysis.
What is a Dataset: Types, Features, and Examples
Dataset is essentially the backbone for all operations, techniques or models used by developers to interpret them. Datasets involve a large amount of data points grouped into one table. Datasets are used in almost all industries today for various reasons. In this day and age, to train the younger generation to interact effectively with Datasets, many Universities publicly release their Datasets for example UCI and websites like Kaggle and even GitHub release datasets that developers can work with to get the necessary outputs.
Table of Content
- What is a Dataset?
- Types of Datasets
- Properties of Dataset
- Features of a Dataset
- Examples
- How to Create a Dataset
- Method 1: Using Python Code
- Method 2: Using Generative AI Tools
- Methods Used in Datasets
- Data vs. Datasets vs. Database
- Conclusion
- FAQs on Datasets