Dresses_Attribute_Sales
Donated on 2/18/2014
This dataset contain Attributes of dresses and their recommendations according to their sales.Sales are monitor on the basis of alternate days.
Dataset Characteristics
Text
Subject Area
Computer Science
Associated Tasks
Classification, Clustering
Feature Type
-
# Instances
501
# Features
13
Dataset Information
Additional Information
Style, Price, Rating, Size, Season, NeckLine, SleeveLength, waiseline, Material, FabricType, Decoration, Pattern, Type, Recommendation are Attributes in dataset.
Has Missing Values?
Yes
Variable 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
Dataset Files
| File | Size |
|---|---|
| Dresses_Attribute_Sales.rar | 5.6 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset dresses_attribute_sales = fetch_ucirepo(id=289) # data (as pandas dataframes) X = dresses_attribute_sales.data.features y = dresses_attribute_sales.data.targets # metadata print(dresses_attribute_sales.metadata) # variable information print(dresses_attribute_sales.variables)
Usman, M. & Ahmed, A. (2014). Dresses_Attribute_Sales [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C56W3V.
Creators
Muhammad Usman
Adeel Ahmed
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.