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Case study for a famous bank marketing data set

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alexkataev/Case-Study-UCI-Bank-Marketing-Dataset

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Case-Study-UCI-Bank-Marketing-Dataset

  1. 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]
  2. bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.
  3. bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs).
  4. bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs).

For this case study I used the dataset 1 - bank-additional-full.csv

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute information

Input variables

Bank client data

  1. age (numeric)
  2. job : type of job (categorical)
  3. marital : marital status (categorical)
  4. education (categorical)
  5. default : has credit in default? (categorical)
  6. housing : has housing loan? (categorical)
  7. loan : has personal loan? (categorical)

Related with the last contact of the current campaign

  1. contact : contact communication type (categorical)
  2. month : last contact month of year (categorical)
  3. day_of_week : last contact day of the week (categorical)
  4. duration : last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Other attributes

  1. campaign : number of contacts performed during this campaign and for this client (numeric, includes last contact)
  2. pdays : number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
  3. previous : number of contacts performed before this campaign and for this client (numeric)
  4. poutcome : outcome of the previous marketing campaign (categorical)

Social and economic context attributes

  1. emp.var.rate : employment variation rate - quarterly indicator (numeric)
  2. cons.price.idx : consumer price index - monthly indicator (numeric)
  3. cons.conf.idx : consumer confidence index - monthly indicator (numeric)
  4. euribor3m : euribor 3 month rate - daily indicator (numeric)
  5. nr.employed : number of employees - quarterly indicator (numeric)

Output variable (desired target)

  1. y - has the client subscribed a term deposit? (binary: 'yes','no')