[SOLVED] Logistic-Regression - BANKING INSURANCE PRODUCT – PHASE 1

Description

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.
  • 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.)
  • 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.
  • Rank these odds ratios by magnitude.
  • Interpret only the highest magnitude odds ratio.
  • 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.
    • List both which variables meet and do not meet the needed assumption for continuous variables.
    • (HINT: Do not get overly mathematical here. Just report what you find; do not teach.)
  • Provide a summary of important data considerations as follows:
    • Visual representation of which variables have the highest (defined by you for now) amount of missing values.
    • 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.)
    • 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.
  • 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.
  • 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.
  • Except for Branch of Bank, consider anything with more than 10 distinct values as continuous.

                            Name                              Model                                           Role        Description