Churn/Retention prediction without machine learning

This page summarizes the projects mentioned and recommended in the original post on /r/datascience

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  • skope-rules

    machine learning with logical rules in Python

  • You could look into using SkopeRules https://github.com/scikit-learn-contrib/skope-rules which trains a tree based model and picks out the best performing root-to-leaf paths as rules. It's ML under the hood but the final 'model' doesn't represent ML at all, since it would just be an if then statement.

  • xgboost-survival-embeddings

    Improving XGBoost survival analysis with embeddings and debiased estimators

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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