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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
Show all 9 features

107 properties

NumberOfInstances
440
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
2
Number of nominal attributes.
AutoCorrelation
0.62
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
0.91
Entropy of the target attribute values.
DecisionStumpAUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
124.06
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
67.73
Percentage of instances belonging to the most frequent class.
MajorityClassSize
298
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.13
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
170.69
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
12000.3
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.01
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
11.15
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
12647.33
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.13
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
43.06
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
4746.95
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.01
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
153.2
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
4.23
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
5996.33
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1.13
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.1
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
2.54
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.01
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.28
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.77
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
32.27
Percentage of instances belonging to the least frequent class.
MinorityClassSize
142
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
11.11
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
77.78
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
22.22
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.13
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
11.54
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1524.87
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
2.56
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2820.11
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.13
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
20.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
3071.93
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
3.63
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
4854.67
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.13
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
54.69
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
7951.28
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
5.91
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
9503.16
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.71
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

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