Logistic-Regression - BANKING INSURANCE PRODUCT – PHASE 1  - Solved

Banking   Insurance     Product   –          

Phase 1:   IP  –    F1.H1 

 

Purpose    
By responding to this Request for Proposal (RFP), the Proposer agrees that s/he has read and understood all documents within this RFP package.


Background       
The Commercial Banking Corporation (hereafter the “Bank”), acting by and through its department of Customer Services and New Products is seeking proposals for banking services. The Bank ultimately wants to predict which customers will buy a variable rate annuity product.  

 

A variable annuity is a contract between you and an insurance company / bank, under which the insurer agrees to make periodic payments to you, beginning either immediately or at some future date. You purchase a variable annuity contract by making either a single purchase payment or a series of purchase payments.

 

A variable annuity offers a range of investment options. The value of your investment as a variable annuity owner will vary depending on the performance of the investment options you choose. The investment options for a variable annuity are typically mutual funds that invest in stocks, bonds, money market instruments, or some combination of the three. If you are interested in more information, see:

http://www.sec.gov/investor/pubs/varannty.htm

 

The project will be broken down into 3 phases:

•       Phase 1 – Variable Understanding and Assumptions

•       Phase 2 – Variable Selection and Modeling Building

•       Phase 3 – Model Assessment and Prediction

Objective  –       Phase         1       
The scope of services in this phase includes the following:

•       For this phase use only the training data set.

•       Explore the predictor variables individually with the target variable of whether the customer bought the insurance product.

o   Summarize only the significant variables in a table ranking from most significant to least significant – the Bank currently uses 𝛼 = 0.002, but is open to another if you defend your reason.

§  This table should separate out the four possible classes of variables – binary, ordinal, nominal, continuous.

§  (HINT: Explore the predictor variables individually for now since you have not yet accounted for missing values.)

§  (HINT: The downside to software sometimes is displaying a full p-value for ranking. That doesn’t mean you cannot get them through the right commands. As long as you have the same degrees of freedom you can rank on test statistic as well.)

o   In an appendix, include a table with all of the variables ranked by significance.

•       Provide a table of odds ratios for only binary predictor variables in relation to the target variable.

o   Rank these odds ratios by magnitude.

o   Interpret only the highest magnitude odds ratio.

o   Report on any interesting findings.  

§  (HINT: This is open-ended and has no correct answer. However, you should get use to keeping an eye out for what you might deem important or interesting when exploring data to report in an executive summary.)

•       Provide a summary of results around the linearity assumption of continuous variables.

o   List both which variables meet and do not meet the needed assumption for continuous variables.

o   (HINT: Do not get overly mathematical here. Just report what you find; do not teach.)

•       Provide a summary of important data considerations as follows:

o   Visual representation of which variables have the highest (defined by you for now) amount of missing values.

o   List any combinations of variables that you feel have redundant information so the Bank might consider removing them in the future.

§  (HINT: This is open-ended and has no correct answer. For example, presence of a money market account and money market balance.)

o   Report on any interesting findings.

§  (HINT: This is open-ended and has no correct answer. However, you should get use to keeping an eye out for what you might deem important or interesting when exploring data to report in an executive summary. For example, teller visits as well as other variables might represent human contact with the bank as compared to only online contact.)

 

 

 

                 

Data  Provided   
The following two sets of data are provided for the proposal:

•       The training data set insurance_t contains 8,495 observations and 48 variables.

o   All of these customers have been offered the product in the data set under the variable INS, which takes a value of 1 if they bought and 0 if they did not buy.

o   There are 47 variables describing the customer’s attributes before they were offered the new insurance product.

•       The validation data set insurance_v contains 2,124 observations and 48 variables.

•       The table below describes the Roles and Description of the variables found in both data sets.

o   Except for Branch of Bank, consider anything with more than 10 distinct values as continuous.

                 


                            Name                              Model                                           Role        Description   
ACCTAGE     
Input
Age of oldest account
DDA  

DDABAL          DEP     

DEPAMT      

CASHBK        

CHECKS        

DIRDEP         NSF     

NSFAMT       

PHONE         

TELLER         SAV     

SAVBAL        

ATM  

ATMAMT     

POS   

POSAMT       

CD     

CDBAL       IRA      

IRABAL         LOC     

LOCBAL         INV      

INVBAL        

ILS     

ILSBAL        MM     

MMBAL        

MMCRED           MTG    

MTGBAL       

CC      

CCBAL           

CCPURC         SDB     

INCOME       

HMOWN       

LORES          

HMVAL         
Input
Indicator for checking account
Input
Checking account balance
Input
Checking deposits
Input
Total amount deposited
Input
Number of cash back requests
Input
Number of checks written
Input
Indicator for direct deposit
Input
Number of insufficient fund issues
Input
Amount of NSF
Input
Number of telephone banking interactions
Input
Number of teller visit interactions
Input
Indicator for savings account
Input
Savings account balance
Input
Indicator for ATM interaction
Input
Total ATM withdrawal amount
Input
Number of point of sale interactions
Input
Total amount for point of sale interactions
Input
Indicator for certificate of deposit account
Input
CD balance
Input
Indicator for retirement account
Input
IRA balance
Input
Indicator for line of credit
Input
LOC balance
Input
Indicator for investment account
Input
INV balance
Input
Indicator for installment loan
Input
ILS balance
Input
Indicator for money market account
Input
MM balance
Input
Number of money market credits
Input
Indicator for mortgage
Input
MTG balance
Input
Indicator for credit card
Input
CC balance
Input
Number of credit card purchases
Input
Indicator for safety deposit box
Input
Income
Input
Indicator for home ownership
Input
Length of residence in years
Input
Value of home
AGE   
Input
Age
CRSCORE      

MOVED        

INAREA        INS      

BRANCH         RES     
Input
Credit score
Input
Recent address change
Input
Indicator for local address
Target
Indicator for purchase of insurance product
Input
Branch of bank
Input
Area classification