Data
dresses-sales

dresses-sales

active ARFF Publicly available Visibility: public Uploaded 11-04-2016 by Rafael Gomes Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_144 study_98 study_99 study_253
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Author: Muhammad Usman & Adeel Ahmed Source: origin source at [UCI](https://archive.ics.uci.edu/ml/datasets/Dresses_Attribute_Sales) Please cite: [Paper that claims to first have used the data](https://www.researchgate.net/profile/Dalia_Jasim/publication/293464737_main_steps_for_doing_data_mining_project_using_weka/links/56b8782008ae44bb330d2583/main-steps-for-doing-data-mining-project-using-weka.pdf) ####1. Summary This dataset contain attributes of dresses and their recommendations according to their sales. Sales are monitor on the basis of alternate days. The attributes present analyzed are: Recommendation, Style, Price, Rating, Size, Season, NeckLine, SleeveLength, waiseline, Material, FabricType, Decoration, Pattern, Type. In this dataset they are named Class(target) and then subsequently V2 - V13. Contact: ``` Muhammad Usman & Adeel Ahmed, usman.madspot '@' gmail.com adeel.ahmed92 '@' gmail.com, Air University, Students at Air University. ``` ####2: Attribute Information: ``` Recommendation:0,1 Style: Bohemia,brief,casual,cute,fashion,flare,novelty,OL,party,sexy,vintage,work. Price:Low,Average,Medium,High,Very-High Rating:1-5 Size:S,M,L,XL,Free Season:Autumn,winter,Spring,Summer NeckLine:O-neck,backless,board-neck,Bowneck,halter,mandarin-collor,open,peterpan-collor,ruffled,scoop,slash-neck,square-collar,sweetheart,turndowncollar,V-neck. SleeveLength:full,half,halfsleeves,butterfly,sleveless,short,threequarter,turndown,null waiseline:dropped,empire,natural,princess,null. Material:wool,cotton,mix etc FabricType:shafoon,dobby,popline,satin,knitted,jersey,flannel,corduroy etc Decoration:applique,beading,bow,button,cascading,crystal,draped,embroridary,feathers,flowers etc Pattern type: solid,animal,dot,leapard etc ```

13 features

Class (target)nominal2 unique values
0 missing
V2nominal13 unique values
0 missing
V3nominal7 unique values
2 missing
V4numeric17 unique values
0 missing
V5nominal7 unique values
0 missing
V6nominal8 unique values
2 missing
V7nominal16 unique values
3 missing
V8nominal17 unique values
2 missing
V9nominal4 unique values
87 missing
V10nominal23 unique values
128 missing
V11nominal22 unique values
266 missing
V12nominal24 unique values
236 missing
V13nominal14 unique values
109 missing
Show all 13 features

62 properties

NumberOfInstances
500
Number of instances (rows) of the dataset.
NumberOfFeatures
13
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
835
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
401
Number of instances with at least one value missing.
NumberOfNumericFeatures
1
Number of numeric attributes.
NumberOfSymbolicFeatures
12
Number of nominal attributes.
Quartile3KurtosisOfNumericAtts
-0.58
Third quartile of kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
3.53
Maximum of means among attributes of the numeric type.
MinMutualInformation
0.01
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
12.85
Percentage of missing values.
Quartile3MeansOfNumericAtts
3.53
Third quartile of means among attributes of the numeric type.
MaxMutualInformation
0.05
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
7.69
Percentage of numeric attributes.
Quartile3MutualInformation
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
24
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.16
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
92.31
Percentage of nominal attributes.
Quartile3SkewnessOfNumericAtts
-1.16
Third quartile of skewness among attributes of the numeric type.
MaxSkewnessOfNumericAtts
-1.16
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.01
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
1.98
First quartile of entropy among attributes.
Quartile3StdDevOfNumericAtts
2.01
Third quartile of standard deviation of attributes of the numeric type.
MaxStdDevOfNumericAtts
2.01
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
42
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-0.58
First quartile of kurtosis among attributes of the numeric type.
StdvNominalAttDistinctValues
7.55
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanAttributeEntropy
2.23
Average entropy of the attributes.
MinorityClassSize
210
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
3.53
First quartile of means among attributes of the numeric type.
MeanKurtosisOfNumericAtts
-0.58
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-1.16
First quartile of skewness among attributes of the numeric type.
MeanMeansOfNumericAtts
3.53
Mean of means among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2.01
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.47
Average class difference between consecutive instances.
MeanMutualInformation
0.03
Average mutual information between the nominal attributes and the target attribute.
Quartile2AttributeEntropy
2.32
Second quartile (Median) of entropy among attributes.
ClassEntropy
0.98
Entropy of the target attribute values.
MeanNoiseToSignalRatio
68.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2KurtosisOfNumericAtts
-0.58
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0.03
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
13.08
Average number of distinct values among the attributes of the nominal type.
Quartile2MeansOfNumericAtts
3.53
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
30.47
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
-1.16
Mean skewness among attributes of the numeric type.
Quartile2MutualInformation
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassPercentage
58
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
2.01
Mean standard deviation of attributes of the numeric type.
Quartile2SkewnessOfNumericAtts
-1.16
Second quartile (Median) of skewness among attributes of the numeric type.
MajorityClassSize
290
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1.37
Minimal entropy among attributes.
Quartile2StdDevOfNumericAtts
2.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxAttributeEntropy
2.81
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-0.58
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
7.69
Percentage of binary attributes.
Quartile3AttributeEntropy
2.57
Third quartile of entropy among attributes.
MaxKurtosisOfNumericAtts
-0.58
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
3.53
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
80.2
Percentage of instances having missing values.
Show all 62 properties

18 tasks

Supervised Classification on dresses-sales
19100 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on dresses-sales
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on dresses-sales
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on dresses-sales
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Supervised Classification on dresses-sales
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
Learning Curve on dresses-sales
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - target_feature: Class
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Clustering on dresses-sales
0 runs - estimation_procedure: 50 times Clustering
Define a new task