This is the repository used to exploit banking marketing data
Here, we conduct analysis and comment on the marketing campaigns of the bank, and also give some advice to the bank in the next marketing to improve marketing effectiveness. In addition, we also use machine learning knowledge to predict whether a customer is a potential or not potential customer, so that the bank can decide whether to market that customer or not.
| STT | Name | Major |
|---|---|---|
| 1 | Nguyen Truong Lau | Computer Science |
| 2 | Dang Khac Loc | Computer Science |
| 3 | Bui Duc Cuong | Computer Science |
Bank Marketing Data Set
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
Source: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
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 ('yes') or not ('no') subscribed.
There are four datasets:
- bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]
- bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.
- bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs).
- bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs).
The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).