How Couchbase Simplifies Data Science (Part 2)

In this article, we learned how to perform exploratory data analysis using the Couchbase Query service, efficiently read big training datasets using the Python SDK, and easily store them in a pandas dataframe suitable for machine learning. We also saw how to store ML models, their metadata, and predictions in Couchbase and use the Query charts for analyzing predictions. Additionally, we learned about what-if analysis using Couchbase Query and Analytics services, including creating scopes and collections, reading data from Couchbase, predicting churn rates, and visualizing results using query charts. The article concludes that Couchbase makes data science easy by providing a platform to store raw data, features, ML models, their metadata, and predictions on the same cluster as running Query and Analytics services.