Data
Bank-Marketing-Dataset

Bank-Marketing-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context Find the best strategies to improve for the next marketing campaign. How can the financial institution have a greater effectiveness for future marketing campaigns? In order to answer this, we have to analyze the last marketing campaign the bank performed and identify the patterns that will help us find conclusions in order to develop future strategies. 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

17 features

agenumeric76 unique values
0 missing
jobstring12 unique values
0 missing
maritalstring3 unique values
0 missing
educationstring4 unique values
0 missing
defaultstring2 unique values
0 missing
balancenumeric3805 unique values
0 missing
housingstring2 unique values
0 missing
loanstring2 unique values
0 missing
contactstring3 unique values
0 missing
daynumeric31 unique values
0 missing
monthstring12 unique values
0 missing
durationnumeric1428 unique values
0 missing
campaignnumeric36 unique values
0 missing
pdaysnumeric472 unique values
0 missing
previousnumeric34 unique values
0 missing
poutcomestring4 unique values
0 missing
depositstring2 unique values
0 missing
Show all 17 features

19 properties

NumberOfInstances
11162
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
7
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfNumericFeatures
41.18
Percentage of numeric attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
Show all 19 properties

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