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GettingYourResearchProjectStarted .pdf


Nombre del archivo original: GettingYourResearchProjectStarted.pdf

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Getting Your Research Project Started
(Week 1)

Codebook


Information about the search and selected dataset:

Source:
[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

Data Set 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 ('yes') or not ('no') subscribed.
There are four datasets:
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).
The smallest datasets are provided to test more computationally demanding machine learning
algorithms (e.g., SVM).

Summary of Variables:
Input variables:
# bank client data:
1 - age (numeric)
2 - job : type of job (categorical:
'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','
technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means
divorced or widowed)

4 - education (categorical:
'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - housing: has housing loan? (categorical: 'no','yes','unknown')
7 - loan: has personal loan? (categorical: 'no','yes','unknown')
# related with the last contact of the current campaign:
8 - contact: contact communication type (categorical: 'cellular','telephone')
9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
11 - 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:
12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes
last contact)
13 - 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)
14 - previous: number of contacts performed before this campaign and for this client (numeric)
15 - poutcome: outcome of the previous marketing campaign (categorical:
'failure','nonexistent','success')
# social and economic context attributes
16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
17 - cons.price.idx: consumer price index - monthly indicator (numeric)
18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)
19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
20 - nr.employed: number of employees - quarterly indicator (numeric)
Output variable (desired target):
21 - y - has the client subscribed a term deposit? (binary: 'yes','no')



Questions and hypothesis:

Is there a relation between education and the subscription of the term deposit?
Is there a relation between marital status and the subscription of the term deposit?
Is there a relation between age and the subscription of the term deposit?
Is there a relation between type of contact (campaign) and the subscription of the term deposit?
Is there a relation between the number of days that passed by after the client was last contacted from a
previous campaign and the subscription of the term deposit?
Is there a relation between week/day and the subscription of the term deposit?
The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).



You can download from (the dataset):

https://archive.ics.uci.edu/ml/machine-learning-databases/00222/


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