Below are several example notebooks that demonstrate how you can use the Virtualitics API in your workflow. Click on the images below to open up the corresponding notebook.

Exploring eCommerce Data

This example gives an introduction to using the Virtualitics API to make functional calls to VIP from your notebook. We focus on loading data, running our built-in ML routines, and leveraging Python to create custom features that can then be fed directly into VIP.

Exploring eCommerce data

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 VIP. We combine typical data science libraries like scikit-learn and TensorFlow with the advanced visualizations created in VIP to look inside the ML “black box” and understand what the models are learning from the data. Furthermore, we use visualizations to very quickly and easily optimize the model.

Explainable ML

Exploring Stock Price Data

This notebook showcases the flexibility that the API adds to VIP by creating a dataset using pandas-datareader, loading the data into VIP to visualize it, then engineering new features based on those visualizations. We also train a model to predict stock prices, and analyze the model output in VIP.

Exploring Stock Market Data

Using VIP’s Network Extractor on ETF Data

This notebook demonstrates how you can use VIP’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.

ETF Network Extractor

Please check back in again soon as we will continue to upload new notebooks.