Lately I’ve been experimenting with utilizing the Analytics Extensions in Tableau Desktop. I haven’t quite seen anyone incorporate any Keras Deep Learning models yet, so I thought it would be a good challenge to explore the possibilities. Below I used a data set containing flight data for a handful of airports to try to predict whether or not a future flight will be delayed with a Keras Deep Learning model. Then I will deploy the model to Tableau Desktop using the TabPy package.
You can find a link to my GitHub Repo.
Here’s the tutorial… Enjoy!
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Here is a simple tutorial for using the R statistical language in Tableau for more advanced ML features including Clustering.
Although Tableau has recently introduced some Clustering functionality, I wanted to explore connecting my Tableau workbook with the R statistical language for a more nuanced and tunable approach. With R I can now do things such as set the seed for reproducibility, scale my factors, tune the hyper parameters such as number of starting cluster centers, tune the maximum number of iterations, and even choose the clustering algorithm from the kmeans() function in R. The other plus to note here…
Training, Deploying, and Automating a Deep Neural Network on the Google Cloud Platform… ( for free! )
There are already so many tutorials on sentiment analysis using Twitter. So before I start, let me first explain how this tutorial is different than the rest:
A tutorial on how to access the Spotify API specifically for podcast data and what you can potentially do with it! For this project I will show how to gather podcast data for every show and episode related to a search term such as “data science”.
As a musician the Spotify API has been my go to place to find and play around with music data. It’s awesome! As of late Spotify has also been putting a lot of effort toward their enormous podcast library. True crime lovers rejoice — there seems to be years worth of entertaining and informative…
Data Scientist, Mathematician, Educator, and Musician