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dummy dataset

Creating a dummy dataset is a common practice in data science and machine learning, especially when real data is unavailable or unsuitable for sharing. Dummy datasets can be used for testing, evaluation, and training machine learning models without the need for real-world data, which may be sensitive or confidential.

One popular method to create dummy datasets in Python is by using the make_classification function from the Sklearn library. This function allows you to generate a numerical dataset with a specified number of samples and features. You can customize the dataset by defining parameters such as the number of informative and redundant features, which can help simulate various scenarios for model testing 15.

Another option is to use the Faker library, which generates fake data for various purposes, including filling databases and testing applications. This library is particularly useful when you need realistic-looking data without using actual sensitive information 4.

Overall, dummy datasets are valuable tools for data professionals to ensure their models are robust and effective in different conditions.

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 Towards Data Science

Two cool ways to create dummy datasets. Photo by Alice Dietrich on Unsplash Usually, for executing/testing a pipeline, we need to provide it with some dummy data. However, finding a good dataset can ...

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 Python in Plain English

A guide on generating dummy data using the Faker library. Continue reading on Python in Plain English

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 Daily Dose of Data Science

Often for testing/building a data pipeline, we may need some dummy data. With Sklearn, you can easily create a dummy dataset for regression, classification, and clustering tasks. More info here: Sklea...

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 Analytics Vidhya

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7.1. Toy datasets

 Scikit-learn User Guide

scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are usefu......

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