Data
kdd_internet_usage

kdd_internet_usage

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized binarized_regression_problem mythbusting_1 study_1 study_144 study_15 study_20 study_41
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

69 features

Who_Pays_for_Access_Work (target)nominal2 unique values
0 missing
Actual_Timenominal46 unique values
0 missing
Agenominal77 unique values
0 missing
Community_Buildingnominal4 unique values
0 missing
Community_Membership_Familynominal2 unique values
0 missing
Community_Membership_Hobbiesnominal2 unique values
0 missing
Community_Membership_Nonenominal2 unique values
0 missing
Community_Membership_Othernominal2 unique values
0 missing
Community_Membership_Politicalnominal2 unique values
0 missing
Community_Membership_Professionalnominal2 unique values
0 missing
Community_Membership_Religiousnominal2 unique values
0 missing
Community_Membership_Supportnominal2 unique values
0 missing
Countrynominal129 unique values
0 missing
Disability_Cognitivenominal2 unique values
0 missing
Disability_Hearingnominal2 unique values
0 missing
Disability_Motornominal2 unique values
0 missing
Disability_Not_Impairednominal2 unique values
0 missing
Disability_Not_Saynominal2 unique values
0 missing
Disability_Visionnominal2 unique values
0 missing
Education_Attainmentnominal9 unique values
0 missing
Falsification_of_Informationnominal7 unique values
0 missing
Gendernominal2 unique values
0 missing
Household_Incomenominal9 unique values
0 missing
How_You_Heard_About_Survey_Bannernominal2 unique values
0 missing
How_You_Heard_About_Survey_Friendnominal2 unique values
0 missing
How_You_Heard_About_Survey_Mailing_Listnominal2 unique values
0 missing
How_You_Heard_About_Survey_Othersnominal2 unique values
0 missing
How_You_Heard_About_Survey_Printed_Medianominal2 unique values
0 missing
How_You_Heard_About_Survey_Remeberednominal2 unique values
0 missing
How_You_Heard_About_Survey_Search_Enginenominal2 unique values
0 missing
How_You_Heard_About_Survey_Usenet_Newsnominal2 unique values
0 missing
How_You_Heard_About_Survey_WWW_Pagenominal2 unique values
0 missing
Major_Geographical_Locationnominal10 unique values
0 missing
Major_Occupationnominal5 unique values
0 missing
Marital_Statusnominal7 unique values
0 missing
Most_Import_Issue_Facing_the_Internetnominal9 unique values
0 missing
Opinions_on_Censorshipnominal4 unique values
0 missing
Primary_Computing_Platformnominal11 unique values
2699 missing
Primary_Languagenominal119 unique values
0 missing
Primary_Place_of_WWW_Accessnominal9 unique values
0 missing
Racenominal8 unique values
0 missing
Not_Purchasing_Bad_experiencenominal2 unique values
0 missing
Not_Purchasing_Bad_pressnominal2 unique values
0 missing
Not_Purchasing_Cant_findnominal2 unique values
0 missing
Not_Purchasing_Company_policynominal2 unique values
0 missing
Not_Purchasing_Easier_locallynominal2 unique values
0 missing
Not_Purchasing_Enough_infonominal2 unique values
0 missing
Not_Purchasing_Judge_qualitynominal2 unique values
0 missing
Not_Purchasing_Never_triednominal2 unique values
0 missing
Not_Purchasing_No_creditnominal2 unique values
0 missing
Not_Purchasing_Not_applicablenominal2 unique values
0 missing
Not_Purchasing_Not_optionnominal2 unique values
0 missing
Not_Purchasing_Othernominal2 unique values
0 missing
Not_Purchasing_Prefer_peoplenominal2 unique values
0 missing
Not_Purchasing_Privacynominal2 unique values
0 missing
Not_Purchasing_Receiptnominal2 unique values
0 missing
Not_Purchasing_Securitynominal2 unique values
0 missing
Not_Purchasing_Too_complicatednominal2 unique values
0 missing
Not_Purchasing_Uncomfortablenominal2 unique values
0 missing
Not_Purchasing_Unfamiliar_vendornominal2 unique values
0 missing
Registered_to_Votenominal4 unique values
0 missing
Sexual_Preferencenominal6 unique values
0 missing
Web_Orderingnominal3 unique values
0 missing
Web_Page_Creationnominal3 unique values
0 missing
Who_Pays_for_Access_Dont_Knownominal2 unique values
0 missing
Who_Pays_for_Access_Othernominal2 unique values
0 missing
Who_Pays_for_Access_Parentsnominal2 unique values
0 missing
Who_Pays_for_Access_Schoolnominal2 unique values
0 missing
Who_Pays_for_Access_Selfnominal2 unique values
0 missing

19 properties

10108
Number of instances (rows) of the dataset.
69
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
2699
Number of missing values in the dataset.
2699
Number of instances with at least one value missing.
0
Number of numeric attributes.
69
Number of nominal attributes.
71.01
Percentage of binary attributes.
26.7
Percentage of instances having missing values.
0.61
Average class difference between consecutive instances.
0.39
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
73.14
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.
7393
Number of instances belonging to the most frequent class.
26.86
Percentage of instances belonging to the least frequent class.
2715
Number of instances belonging to the least frequent class.
49
Number of binary attributes.

17 tasks

343 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Who_Pays_for_Access_Work
209 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Who_Pays_for_Access_Work
0 runs - estimation_procedure: 33% Holdout set - target_feature: Who_Pays_for_Access_Work
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_precision - target_feature: Country
70 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Who_Pays_for_Access_Work
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Who_Pays_for_Access_Work
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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