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

19 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 numeric attributes.
0.03
Number of attributes divided by the number of instances.
92.31
Percentage of nominal attributes.
58
Percentage of instances belonging to the most frequent class.
290
Number of instances belonging to the most frequent class.
42
Percentage of instances belonging to the least frequent class.
210
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
7.69
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.47
Average class difference between consecutive instances.

4 tasks

50 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
49 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Define a new task