CSV (Comma-Separated Values):
A plain text format where each row represents an instance or example, and columns represent features. The last column typically contains the class labels. An example is below (code):
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JSON (JavaScript Object Notation):
A lightweight data interchange format that is easy for humans to read and write. It can represent hierarchical structures, making it suitable for complex data.
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XML (eXtensible Markup Language):
Similar to JSON, XML is a markup language that can represent structured data. It's often used in web applications and can be adapted for classification datasets.
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Excel Spreadsheets:
Datasets can be stored in Excel files, with each sheet representing a different dataset or split of the data.
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HDF5 (Hierarchical Data Format version 5):
A file format and set of tools for managing complex data. It's commonly used for large datasets and supports hierarchical structures.
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ARFF (Attribute-Relation File Format):
A plain text file format specifically designed for representing datasets used by the machine learning community.
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Database Tables:
Datasets can be stored in relational databases, and SQL queries can be used to extract the required data for training and testing.
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LibSVM Format:
A simple text-based format used for datasets that can be efficiently used with the LIBSVM tool, which is commonly used for support vector machine (SVM) classification.
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Image Datasets:
In computer vision tasks, datasets may consist of images. Image datasets can be organized in folders, where each folder represents a class, and images within the folder belong to that class.
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Text Datasets:
For natural language processing tasks, datasets may consist of text documents. These can be in plain text format, or in more specialized formats like the CoNLL or TSV formats for labeled text data.
