BANK:
Bank Churn data set
Description
Businesses like banks which provide service have to worry about problem of 'Churn' i.e. customers leaving and joining another service provider. It is important to understand which aspects of the service influence a customer's decision in this regard. Management can concentrate efforts on improvement of service, keeping in mind these priorities.
Format
A data frame with 245 observations on the following 20 variables.
Serial_Number- Serial Number
Response- Response (1\: deserter, 0\: Loyal)
Branch- Branch code
Occupation- Occupation of Customer
Age- Age in Years
Sex- Gender
Pleasant_Ambiance- Pleasant Ambiance ACT1
Comfortable_seating_arrangement- Comfortable seating arrangement ACT2
Immediate_attenttion- Immediate attenttion ACT4
Good_Response_on_Phone- Good Response on Phone ACT5
Errors_in_Passbook_entries- Errors in Passbook entries ACT10
Time_to_issue_cheque_book- Time to issue cheque book ACT14
Time_to_sanction_loan- Time to sanction loan ACT16
Time_to_clear_outstation_cheques- Time to clear outstation cheques ACT17
Issue_of_clean_currency_notes- Issue of clean currency notes ACT24
Facility_to_pay_bills- Facility to pay bills ACT26
Distance_to_residence- Distance to residence ACT28
Distance_to_workplace- Distance to workplace ACT30
Courteous_staff_behaviour- Courteous staff behaviour ACT31
Enough_parking_place- Enough parking place ACT32
Source
http://ces.iisc.ernet.in/hpg/nvjoshi/statspunedatabook/databook.htmlDetails
Explore the application of logistic regression and contingency tables for this data set.