This dissertation has two parts:
The first part includes the description and analysis of Big data Analytics, its concepts,
methods and analytical tools as well an extended analysis for Predictive Analytics,
Classification Problem and literature review of classification Models predicting
customer churn.
Second part includes the creation of two models which predict customer churn with
classification especially Logistic Regression and Radial Basic Function and compare their
performance.
The overall accuracy of the model, Receiver Operating Characteristic curve and Area
Under the Receiver Operating Characteristic Curve is used as the evaluation metrics for
this research to identify the best classifier.
"Big Data Analytics in Banks: Comparison of Classification Models in predicting customers churn" Περιγραφή: Dissertation_Open University of Cyprus-Chara Gavrielidou.pdf (pdf)
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"Big Data Analytics in Banks: Comparison of Classification Models in predicting customers churn" - Identifier: 178
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