Data
wholesale-customers

wholesale-customers

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Author: Margarida G. M. S. Cardoso Source: UCI Please cite: Abreu, N. (2011). Analise do perfil do cliente Recheio e desenvolvimento de um sistema promocional. Mestrado em Marketing, ISCTE-IUL, Lisbon. * Title: Wholesale customers Data Set * Abstract: The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories * Source: Margarida G. M. S. Cardoso, margarida.cardoso '@' iscte.pt, ISCTE-IUL, Lisbon, Portugal * Attribute Information: 1) FRESH: annual spending (m.u.) on fresh products (Continuous); 2) MILK: annual spending (m.u.) on milk products (Continuous); 3) GROCERY: annual spending (m.u.)on grocery products (Continuous); 4) FROZEN: annual spending (m.u.)on frozen products (Continuous) 5) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) 6) DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous); 7) CHANNEL: customers' Channel - Horeca (Hotel/Restaurant/Café) or Retail channel (Nominal) 8) REGION: customers' Region - Lisbon, Porto or Other (Nominal) Descriptive Statistics: (Minimum, Maximum, Mean, Std. Deviation) FRESH ( 3, 112151, 12000.30, 12647.329) MILK (55, 73498, 5796.27, 7380.377) GROCERY (3, 92780, 7951.28, 9503.163) FROZEN (25, 60869, 3071.93, 4854.673) DETERGENTS_PAPER (3, 40827, 2881.49, 4767.854) DELICATESSEN (3, 47943, 1524.87, 2820.106) REGION Frequency Lisbon 77 Oporto 47 Other Region 316 Total 440 CHANNEL Frequency Horeca 298 Retail 142 Total 440 * Relevant Papers: Cardoso, Margarida G.M.S. (2013). Logical discriminant models – Chapter 8 in Quantitative Modeling in Marketing and Management Edited by Luiz Moutinho and Kun-Huang Huarng. World Scientific. p. 223-253. ISBN 978-9814407717 Jean-Patrick Baudry, Margarida Cardoso, Gilles Celeux, Maria José Amorim, Ana Sousa Ferreira (2012). Enhancing the selection of a model-based clustering with external qualitative variables. RESEARCH REPORT N° 8124, October 2012, Project-Team SELECT. INRIA Saclay - ÃŽle-de-France, Projet select, Université Paris-Sud 11

9 features

Channel (target)nominal2 unique values
0 missing
V1numeric3 unique values
0 missing
V2numeric433 unique values
0 missing
V3numeric421 unique values
0 missing
V4numeric430 unique values
0 missing
V5numeric426 unique values
0 missing
V6numeric417 unique values
0 missing
V7numeric403 unique values
0 missing
Regionnominal3 unique values
0 missing

62 properties

440
Number of instances (rows) of the dataset.
9
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.
7
Number of numeric attributes.
2
Number of nominal attributes.
0.01
Average mutual information between the nominal attributes and the target attribute.
2820.11
First quartile of standard deviation of attributes of the numeric type.
0.62
Average class difference between consecutive instances.
153.2
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.13
Second quartile (Median) of entropy among attributes.
0.91
Entropy of the target attribute values.
2.5
Average number of distinct values among the attributes of the nominal type.
20.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Number of attributes divided by the number of instances.
4.23
Mean skewness among attributes of the numeric type.
3071.93
Second quartile (Median) of means among attributes of the numeric type.
124.06
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5996.33
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
67.73
Percentage of instances belonging to the most frequent class.
1.13
Minimal entropy among attributes.
3.63
Second quartile (Median) of skewness among attributes of the numeric type.
298
Number of instances belonging to the most frequent class.
-0.1
Minimum kurtosis among attributes of the numeric type.
11.11
Percentage of binary attributes.
4854.67
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.13
Maximum entropy among attributes.
2.54
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.13
Third quartile of entropy among attributes.
170.69
Maximum kurtosis among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
54.69
Third quartile of kurtosis among attributes of the numeric type.
12000.3
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
77.78
Percentage of numeric attributes.
7951.28
Third quartile of means among attributes of the numeric type.
0.01
Maximum mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
-1.28
Minimum skewness among attributes of the numeric type.
22.22
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
11.15
Maximum skewness among attributes of the numeric type.
0.77
Minimum standard deviation of attributes of the numeric type.
1.13
First quartile of entropy among attributes.
5.91
Third quartile of skewness among attributes of the numeric type.
12647.33
Maximum standard deviation of attributes of the numeric type.
32.27
Percentage of instances belonging to the least frequent class.
11.54
First quartile of kurtosis among attributes of the numeric type.
9503.16
Third quartile of standard deviation of attributes of the numeric type.
1.13
Average entropy of the attributes.
142
Number of instances belonging to the least frequent class.
1524.87
First quartile of means among attributes of the numeric type.
0.71
Standard deviation of the number of distinct values among attributes of the nominal type.
43.06
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
4746.95
Mean of means among attributes of the numeric type.
2.56
First quartile of skewness among attributes of the numeric type.

6 tasks

81 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Region
80 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Channel
0 runs - estimation_procedure: 33% Holdout set - target_feature: Channel
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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