Bank

Telemarketing (yes/no) - loan acquisition bank campaign

This is a bank loan case with a binary (yes / no) answer option to accept or reject the loan in a tele marketing campaign. The detail description of the campaign and its data source is as follows:

Relevant Information:

The data is related with direct marketing campaigns of a Portuguese banking institution.
The marketing campaigns were based on phone calls. Often, more than one contact to the
same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

key Question

The positive answers rate are 12% in the real scenario; ¿ Which variable combination will reduce
the negative answers (reduce the negative predictive value)? and which is the importance and direction (positive or negative ) influence on the decision

THE XGBOOST ALGORITHM WILL BE USED TO ANSWER THIS QUESTION

DATA SOURCE

This dataset is public available for research. The details are described in [Moro et al., 2011]. Please include this citation if you plan to use this database:

 [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing:
 An Application of the CRISP-DM Methodology.<br> 
 In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - 
 ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.<br><br>

Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt

1. Title: Bank Marketing

2. Sources

   Created by: Paulo Cortez (Univ. Minho) and Sérgio Moro (ISCTE-IUL) @ 2012
   

3. Past Usage:
   

The full dataset was described and analyzed in:

S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology.
In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

There are two datasets:

  1) bank-full.csv with all examples, ordered by date (from May 2008 to November 2010).
  2) DataSample.csv with 10% of the examples (4521), randomly selected from bank-full.csv.
  
  THE ORIGINAL DATA SET HAS 45,211 OBSERVATIONS AND 17 VARIABLES; 10% WILL BE UPLOADED TO <
  UNDERSTAND THE DATA STRUCTURE. File name: DataSample.csv , if the original data set is required
  write us : info@gssg.com.co
  
  NOTE: Two different lines of code will be available: a) XGBoost_2.R is the R code to run with the original
  data set: bank-full.csv ; it takes more time running! b) XGBoost_bank.R is the shiny code to runwith the DataSample.csv data set.
  To check the shiny site go to:  

https://inisghtdiscovery.shinyapps.io/BankLoan/