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bank-marketing

bank-marketing

active ARFF Publicly available Visibility: public Uploaded 21-06-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description: Author: Paulo Cortez, Sergio Moro Source: [UCI](https://archive.ics.uci.edu/ml/datasets/bank+marketing) Please cite: 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, Guimaraes, Portugal, October, 2011. EUROSIS. 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 (or not) subscribed. The classification goal is to predict if the client will subscribe a term deposit (variable y). ### Attribute information For more information, read [Moro et al., 2011]. Input variables: - bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur", "student","blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") - related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) - other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") - output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no")

8 features

Class (target)nominal2 unique values
0 missing
V1numeric76 unique values
0 missing
V6nominal3723 unique values
0 missing
V10nominal31 unique values
0 missing
V12nominal1431 unique values
0 missing
V13nominal36 unique values
0 missing
V14numeric461 unique values
0 missing
V15nominal31 unique values
0 missing

19 properties

10578
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
6
Number of nominal attributes.
50
Percentage of instances belonging to the least frequent class.
5289
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
12.5
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
25
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
75
Percentage of nominal attributes.
5289
Number of instances belonging to the most frequent class.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
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