Bank Customer Churn Prediction : A comparison between classification and evaluation methods

Bank Customer Churn Prediction : A comparison between classification and evaluation methods

This study aims to assess which supervised statistical learning method; random forest, logistic regression or K-nearest neighbor, that is the best at predicting banks customer churn. Additionally, the study evaluates which cross-validation set approach; k-Fold cross-validation or leave-one-out cross...

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Bibliographic Details
Main Authors: Tandan, Isabelle, Goteman, Erika
Format: Others
Language:English
Published: Uppsala universitet, Statistiska institutionen 2020
Subjects:
machine learning
cross-validation
k-fold
leave-one-out
random forest
decision trees
k-nearest neighbor
logistic regression
supervised learning
supervised statistical learning
binary classification
customer churn
bank customer churn.
Probability Theory and Statistics
Sannolikhetsteori och statistik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411918

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411918

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