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Data_processing_and_exploratory_analysis.ipynb
README.md
classification_including_comprehensive_features.ipynb
classification_large_churn_cases.ipynb

README.md

Real-time and personalized scoring of customer engagement

This is a project that I worked as a consultant for a startup company that provides a client success analytics platform.

I am not providing any data file in this repository under the NDA.

Three notebooks are included.

1. Data_processing_and_exploratory_analysis.ipynb --> all the tenant companies (SaaS) were considered. Explored how different features are related to one another.

2. classification_including_comprehensive_features.ipynb --> all the tenant companies were considered. The random forest classifier was used to classify churn and non-churn cases.

3. classification_large_churn_cases.ipynb --> only the tenant companies with high churn number were considered.The recall was significantly improved.