Data Set

Data Set

Data Set: 

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  • This data set consists of observations taken from account holders at a large financial services firm. The accounts represent consumers of home equity lines of credit, automobile loans, and other short- to medium-term credit instruments. The BANK data set contains the original data in its raw form. The target variables relate to whether that account holder purchased a new product from the bank in the past year. The data sets contain more than one million rows and 24 columns. The “dataset” column further identifies whether the observation is to be used for training (60% of the observations), validation (20%), and testing (20%).

Client’s Need:

  • Your client would like to embark on a direct marketing campaign to increase bank revenues. To do that, they would like to understand what drives a customer to try new products/ services (B_TGT), understand what drives the total new sales (INT_TGT) as well as the total number of new products and services purchased by customers (CNT_TGT).  Thus, you have been given sample data from which you are to build, validate, and test your predictive models. Your client requires three models, one for each of the three variables they would like to predict to better help them target their direct marketing campaign. Your client would also like to see how your model performs against the test holdout dataset and will use your model’s performance on the test dataset for their long-term consulting relationships as well as to determine whose model will be deployed.

Data Dictionary:

Target Variables

B_TGT New Product (Binary) (yes/no)
INT_TGT New Sales (Interval)
CNT_TGT Count Number New Products


Categorical Inputs

CAT_INPUT1 Account Activity Level
CAT_INPUT2 Customer Value Level


Interval Inputs

RFM1 Average Sales Past Three Years
RFM2 Average Sales Lifetime
RFM3 Average Sales Past Three Years Dir Promo Resp
RFM4 Last Product Purchase Amount
RFM5 Count Purchased Past 3 Years
RFM6 Count Purchased Lifetime
RFM7 Count Purchased Past 3 Years Dir Promo Resp
RFM8 Count Purchased Lifetime Dir Promo Resp
RFM9 Months Since Last Purchase
RFM10 Count Total Promos Past Year
RFM11 Count Direct Promos Past Year
RFM12 Customer Tenure


Demographic Inputs

DEMOG_AGE Customer Age
DEMOG_GENF Female Binary (yes/no)
DEMOG_GENM Male Binary (yes/no)
DEMOG_HO Homeowner Binary (yes/no)
DEMOG_PR Percentage retired in the area


Dataset (NOTE:  you do not to use the test or validation dataset to train your models)

dataset 1 = training dataset

2 = validation dataset

3= test dataset


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