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Apriori algorithm within Association Rule Learning helps the manager of a store to layout how items are placed in the store. It tells us if constumers bought this product, then they are likely to buy this other product. Thus, we could place these products together and customers are more like to buy both products.

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Jupyter Notebook

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The Jupyter notebook is a web-based notebook environment for interactive computing.

Jupyter notebook example

Jupyter notebook, the language-agnostic evolution of IPython notebook

Jupyter notebook is a language-agnostic HTML notebook application for Project Jupyter. In 2015, Jupyter notebook was released as a part of The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic release containing both language-agnostic code, such as the IPython notebook, and language specific code, such as the IPython kernel for Python. As computing spans across many languages, Project Jupyter will continue to develop the language-agnostic Jupyter notebook in this repo and with the help of the community develop language specific kernels which are found in their own discrete repos. [The Big Split™ announcement] [Jupyter Ascending blog post]

Installation

You can find the installation documentation for the Jupyter platform, on ReadTheDocs. The documentation for advanced usage of Jupyter notebook can be found here.

For a local installation, make sure you have pip installed and run:

$ pip install notebook

Usage - Running Jupyter notebook

Running in a local installation

Launch with:

$ jupyter notebook

Running in a remote installation

You need some configuration before starting Jupyter notebook remotely. See Running a notebook server.

Development Installation

See CONTRIBUTING.rst for how to set up a local development installation.

Contributing

If you are interested in contributing to the project, see CONTRIBUTING.rst.

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Apriori algorithm within Association Rule Learning helps the manager of a store to layout how items are placed in the store. It tells us if constumers bought this product, then they are likely to buy this other product. Thus, we could place these products together and customers are more like to buy both products.

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