Commit Inicial

data/tanveer/bank/bank-names.txt

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+Citation Request:
+  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. 
+  In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.
+
+  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.
+
+4. 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. 
+
+   There are two datasets: 
+      1) bank-full.csv with all examples, ordered by date (from May 2008 to November 2010).
+      2) bank.csv with 10% of the examples (4521), randomly selected from bank-full.csv.
+   The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g. SVM).
+
+   The classification goal is to predict if the client will subscribe a term deposit (variable y).
+
+5. Number of Instances: 45211 for bank-full.csv (4521 for bank.csv)
+
+6. Number of Attributes: 16 + output attribute.
+
+7. 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. Missing Attribute Values: None