Data
dresses-sales

dresses-sales

deactivated ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
0 likes downloaded by 10 people , 18 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Muhammad Usman, Adeel Ahmed Source: UCI Please cite: Source: Muhammad Usman & Adeel Ahmed, usman.madspot '@' gmail.com adeel.ahmed92 '@' gmail.com, Air University, Students at Air University. Data Set Information: Style, Price, Rating, Size, Season, NeckLine, SleeveLength, waiseline, Material, FabricType, Decoration, Pattern, Type, Recommendation are Attributes in dataset. Attribute Information: 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 Recommendation:0,1

13 features

Class (target)nominal2 unique values
0 missing
V2nominal13 unique values
0 missing
V3nominal8 unique values
0 missing
V4numeric17 unique values
0 missing
V5nominal7 unique values
0 missing
V6nominal9 unique values
0 missing
V7nominal17 unique values
0 missing
V8nominal18 unique values
0 missing
V9nominal5 unique values
0 missing
V10nominal24 unique values
0 missing
V11nominal23 unique values
0 missing
V12nominal25 unique values
0 missing
V13nominal15 unique values
0 missing

62 properties

500
Number of instances (rows) of the dataset.
13
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.
1
Number of numeric attributes.
12
Number of nominal attributes.
7.69
Percentage of binary attributes.
2.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.87
Maximum entropy among attributes.
-0.58
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
2.57
Third quartile of entropy among attributes.
-0.58
Maximum kurtosis among attributes of the numeric type.
3.53
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.58
Third quartile of kurtosis among attributes of the numeric type.
3.53
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
7.69
Percentage of numeric attributes.
3.53
Third quartile of means among attributes of the numeric type.
0.05
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
92.31
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
25
The maximum number of distinct values among attributes of the nominal type.
-1.16
Minimum skewness among attributes of the numeric type.
2.01
First quartile of entropy among attributes.
-1.16
Third quartile of skewness among attributes of the numeric type.
-1.16
Maximum skewness among attributes of the numeric type.
2.01
Minimum standard deviation of attributes of the numeric type.
-0.58
First quartile of kurtosis among attributes of the numeric type.
2.01
Third quartile of standard deviation of attributes of the numeric type.
2.01
Maximum standard deviation of attributes of the numeric type.
42
Percentage of instances belonging to the least frequent class.
3.53
First quartile of means among attributes of the numeric type.
7.77
Standard deviation of the number of distinct values among attributes of the nominal type.
2.28
Average entropy of the attributes.
210
Number of instances belonging to the least frequent class.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
-0.58
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-1.16
First quartile of skewness among attributes of the numeric type.
3.53
Mean of means among attributes of the numeric type.
2.01
First quartile of standard deviation of attributes of the numeric type.
0.47
Average class difference between consecutive instances.
0.03
Average mutual information between the nominal attributes and the target attribute.
2.33
Second quartile (Median) of entropy among attributes.
0.98
Entropy of the target attribute values.
65.08
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-0.58
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
13.83
Average number of distinct values among the attributes of the nominal type.
3.53
Second quartile (Median) of means among attributes of the numeric type.
28.43
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-1.16
Mean skewness among attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
58
Percentage of instances belonging to the most frequent class.
2.01
Mean standard deviation of attributes of the numeric type.
-1.16
Second quartile (Median) of skewness among attributes of the numeric type.
290
Number of instances belonging to the most frequent class.
1.42
Minimal entropy among attributes.

4 tasks

47 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Define a new task