Below are several example notebooks that demonstrate how you can use the Virtualitics API in your workflow.
Exploring eCommerce Travel Data
This example gives an introduction to using the Virtualitics API to make functional calls to Explore from your notebook. We focus on loading data, running our built-in AI routines, and leveraging Python to create custom features that can then be fed directly into Explore.
Jupyter Notebook: Exploring eCommerce Travel Data.ipynb
Model Visualization and Explainability
In this example, we investigate a more advanced use case of the API by building models in the notebook then visualizing the outputs in Explore. We combine typical data science libraries like scikit-learn and TensorFlow with the advanced visualizations created in Explore to look inside the AI “black box” and understand what the models are learning from the data. Furthermore, we use visualizations to very quickly and easily optimize the model.
Jupyter notebook: Explainable ML.ipynb
HTML: Explainable ML.html
Using Explore's Network Extractor on ETF Data
This notebook demonstrates how you can use Explore's Network Extractor to create a network dataset from a tabular spreadsheet. This can be very useful for analyzing data with many categorical features, and can provide insight about similarities and differences that exist in the data.
Jupyter notebook: Network Extractor with Exchange Traded Funds.ipynb