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How about using Facebook's Prophet package for time series forecasting in Alteryx Designer? With Prophet, you are not stuck with the results of a completely automatic procedure if the forecast is not satisfactory — an analyst with no training in time series methods can improve or tweak forecasts using a variety of easily-interpretable parameters.
With the debut of the Python tool in Alteryx Designer 2018.3, many great macros from PDF table extraction to Parquet file integration are born. As a citizen data scientist, the first instinct is to import some great scikit-learn package into the predictive palette. The question is where to start?
Have you struggled to deploy your predictive models in a timely manner before they become obsolete? This article will show you how Alteryx Promote solves this challenge by deploying your model into a RESTful API that can be called from a wide variety of enterprise applications.
Understanding the topic of a piece of writing is typically an easy task for people. However, there are times where we need to train our computers to find topics in a collection of documents. There might be too many documents for you, a single human, to read through, or you may be interested in discovering underlying themes in a large set of texts. Enter LDA, a popular model for Topic Modeling.
After training a Phrases model with Community texts, I wanted to be able to incorporate the model into Alteryx workflows that I was using to process text, and hopefully even be able to share the model with other Alteryx users. After thinking through this, I realized it was a perfect application for the Python SDK.
Word embeddings are vector representations of words, where more similar words will have similar locations in vector space. First developed by a team of researchers at Google led by Thomas Mikolov, and discussed in the paper Efficient Estimation of Word Representations in Vector Space, word2vec is a popular group of models that produce word embeddings by training shallow neural networks. In this blog post, we apply a word2vec model to the Alteryx Community texts to develop Alteryx-specific word embeddings.
Reproducibility, the open sharing of data, and expanding on the research of others are all at the heart of the scientific process, and we live in an exciting time where it is more possible than ever. This year's Inspire Europe Closing Keynote speaker Dr. Ben Goldacre has recently published a paper examining compliance with the European Commission's guideline that all Clinical Trials registered in the EU Clinical Trials Register must report results to the European Medicines Agency within 12 months of the trial's completion. The bulk of the paper's analysis was performed in the statistical software Stata. With tools like Alteryx or Python, we have easy and open-source ways to process data and derive new knowledge. In this blog, we reproduce some of Goldacre et al.'s analysis in Alteryx and Python and provide both formats for you to further explore the data on your own.
Ever wondered how to build a new analytic tool from scratch using the Alteryx Python SDK, but didn’t know where to start? This blog post takes you through the absolute basics to get you up and running - You’ll be creating brand new tools, connectors and advanced analytics in no time with this step-by-step beginners guide!