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Title: "Big Data Analytics in Banks: Comparison of Classification Models in predicting customers churn"
Authors: Gavrielidou, Chara
metadata.dc.contributor.advisor: Ipsilandis, Pandelis
Keywords: α
Issue Date: 30-May-2021
Abstract: 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.
Supervisor: Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Appears in Collections:ERM Διπλωματικές Εργασίες

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