This story was originally published on HackerNoon at: https://hackernoon.com/16-best-sklearn-datasets-for-building-machine-learning-models.
Sklearn datasets are included as part of the scikit-learn (sklearn) library, so they come pre-installed with the library.
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This story was written by: @datasets. Learn more about this writer by checking @datasets's about page, and for more stories, please visit hackernoon.com.

Sklearn is a Python module for machine learning built on top of SciPy. It is unique due to its wide range of algorithms and ease of use. Data powers machine learning algorithms and scikit-learn. Sklearn offers high quality datasets that are widely used by researchers, practitioners and enthusiasts.

This story was originally published on HackerNoon at: https://hackernoon.com/enhancing-audit-processes-with-advanced-analytical-tools.
Discover how advanced analytical tools streamline audit processes, boosting accuracy and efficiency for tech professionals.
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This story was written by: @devinpartida. Learn more about this writer by checking @devinpartida's about page, and for more stories, please visit hackernoon.com.

Developers can leverage advanced analytics tools to streamline and improve software, compliance and internal controls auditing. Advanced analytics tools like artificial intelligence, complex event processing and data mining enable 100% population testing. They eliminate the need for sampling, thereby reducing bias and error risks. Autonomous technologies like AI are particularly beneficial since they eliminate human error.

This story was originally published on HackerNoon at: https://hackernoon.com/go-clean-to-be-lean-data-optimization-for-improved-business-efficiency.
The article discusses cost optimization with clean data, explaining how businesses can save resources by reducing the workload for data analysts and more.
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This story was written by: @karolisdidziulis. Learn more about this writer by checking @karolisdidziulis's about page, and for more stories, please visit hackernoon.com.

This article discusses cost optimization with clean data. It explains how businesses can save resources by decreasing the load for data analysts, among other opportunities. It also discusses the differences between raw and clean data and who can benefit from switching to the latter. You'll also find 4 ways in which clean data reduces time to value.