Data
dresses-sales

dresses-sales

active ARFF Publicly available Visibility: public Uploaded 11-04-2016 by Rafael G. Mantovani
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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: ####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: Style, Price, Rating, Size, Season, NeckLine, SleeveLength, waiseline, Material, FabricType, Decoration, Pattern, Type, Recommendation. Contact: ``` Muhammad Usman & Adeel Ahmed, usman.madspot '@' gmail.com adeel.ahmed92 '@' gmail.com, Air University, Students at Air University. ``` ####2: 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
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

107 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).
835
Number of missing values in the dataset.
401
Number of instances with at least one value missing.
1
Number of numeric attributes.
12
Number of nominal attributes.
12.85
Percentage of missing values.
-0.58
Third quartile of kurtosis among attributes of the numeric type.
0.47
Average class difference between consecutive instances.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1.16
Maximum skewness among attributes of the numeric type.
2.01
Minimum standard deviation of attributes of the numeric type.
7.69
Percentage of numeric attributes.
3.53
Third quartile of means among attributes of the numeric type.
0.54
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.01
Maximum standard deviation of attributes of the numeric type.
42
Percentage of instances belonging to the least frequent class.
92.31
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.41
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.23
Average entropy of the attributes.
210
Number of instances belonging to the least frequent class.
1.98
First quartile of entropy among attributes.
-1.16
Third quartile of skewness among attributes of the numeric type.
0.06
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.58
Mean kurtosis among attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-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.
0.54
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.53
Mean of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.53
First quartile of means among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.06
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
68.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
-1.16
First quartile of skewness among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.54
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
7.55
Standard deviation of the number of distinct values among attributes of the nominal type.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
13.08
Average number of distinct values among the attributes of the nominal type.
2.01
First quartile of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.41
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-1.16
Mean skewness among attributes of the numeric type.
2.32
Second quartile (Median) of entropy among attributes.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
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
0.44
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
58
Percentage of instances belonging to the most frequent class.
2.01
Mean standard deviation of attributes of the numeric type.
-0.58
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Entropy of the target attribute values.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
290
Number of instances belonging to the most frequent class.
1.37
Minimal entropy among attributes.
3.53
Second quartile (Median) of means among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.81
Maximum entropy among attributes.
-0.58
Minimum kurtosis among attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.39
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.58
Maximum kurtosis among attributes of the numeric type.
3.53
Minimum of means among attributes of the numeric type.
-1.16
Second quartile (Median) of skewness among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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 binary attributes.
2.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
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.
80.2
Percentage of instances having missing values.
2.57
Third quartile of entropy among attributes.
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
30.47
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
24
The maximum number of distinct values among attributes of the nominal type.
-1.16
Minimum skewness among attributes of the numeric type.

11 tasks

9 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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