Documentation
Python SDK Guides
Engagement Prediction

Engagement Prediction

Algorithm
Use Cases

Prerequisites

Before you reach this stage, ensure you’ve:
  1. Created a schema
  1. Integrated code to process your data and installed Begin’s Python SDK
  1. Created a project with the objective “Predict User Engagement”
Learning usually takes about 30 minutes. Once complete, your machine learning algorithm is ready to be used on your mobile device, browser, connected vehicles, or server applications.

Procedure

In order to fetch recommendations, follow the initialization step:

import beginai as bg

worker = bg.BeginWorker(app_id=APP_ID, license_key=LICENSE_KEY)
 
To use your trained algorithm, input the following code

worker.predict_engagement(project uuid, user_id, object_id)
This will return a predicted interaction between the provided user and the object. The possible interactions are:
  • INTERACTS_POSITIVE
  • INTERACTS_NEGATIVE
  • INTERACTS_NEUTRAL
  • NOT_FOUND (in the case one of the parameters provided is not available)
 
If you performed the initial processing in Python, you can still use the algorithm with other SDKs like Android or iOS SDKs.
 
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Note: Begin’s algorithms can learn continuously with additional user data while your code is deployed. Your project can learn from datasets on your mobile application, server or other applications without you having to worry about merging the learning or merging the data. Begin will take care of merging the learning.