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by Patrick Titzler, Margriet Groenendijk | Updated March 28, 2019 - Published August 22, 2018
AnalyticsArtificial intelligenceData science
Jupyter Notebooks is a tool used by many data scientists to wrangle and clean data, visualize data, build and test machine learning models, and even write talks. The reason for this is that the text, code, figures, and tables can be combined, which makes it easy to keep the code structured. This code pattern shows how you can use Jupyter Notebooks in IBM Watson Studio along with the open source Python packages Apache Spark and PixieDust to quickly analyze historical shopping data and produce charts and maps.
Analyzing shopping data can give you a lot of information about customers and products. Although it can give you details about what customers are looking for, often it can be difficult to pull together and analyze the data that you need. Instead of relying on spreadsheets to analyze your data, this code pattern explains how you can analyze historical shopping data in a Jupyter Notebook with the open source Python packages Apache Spark and PixieDust.
To visualize data with Python, there are many packages available, but it might be a little overwhelming when you begin. With PixieDust, you can explore data in a simpler way. PixieDust uses visualization packages to create charts, including matplotlib, bokeh, seaborn, and Brunel. To explore PixieDust, you can use this code pattern where historical shopping data is analyzed with Spark and PixieDust. The data is loaded, cleaned, and then analyzed by creating various charts and maps. Jupyter Notebooks are run in IBM Watson Studio.
When you have completed this code pattern, you should understand how to:
See the README for detailed instructions. These steps explain how to:
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