A few days ago we released Seahorse 1.4, an enhanced version of our machine learning, Big Data manipulation and data visualization product. This release also comes with an SDK – a Scala toolkit for creating new custom operations to be used in Seahorse. As a showcase, we will create a custom Geospatial operation with GeoJson and make a simple Seahorse workflow using it. This use-case is inspired by Example […]
About Adam Jakubowski
This author has yet to write their bio.Meanwhile lets just say that we are proud Adam Jakubowski contributed a whooping 3 entries.
Seahorse provides users with reports on their data at every step in the workflow. A user can view reports after each operation to review the intermediate results. In our reports we provide users with distributions for columns in the form of a histogram for continuous data, and a pie chart for categorical data.
In Seahorse we want to provide our users with accurate distributions for their categorical data. Categorical data can be thought of as possible results of an observation that can take one of K possible outcomes. Some examples: Nationality, Marital Status, Gender, Type of Education.