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For the purposes of this study we removed examples with missing values in any target variable (112 out of 751) and attributes with one distinct value (145 out of 558).", "format": "ARFF", "uploader": "Quay Au", "uploader_id": 2373, "visibility": "public", "creator": null, "contributor": null, "date": "2019-03-14 18:08:02", "update_comment": null, "last_update": "2019-03-14 18:08:02", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/21230439\/file17303e673734.arff", "default_target_attribute": "Outcome_M1,Outcome_M2,Outcome_M3,Outcome_M4,Outcome_M5,Outcome_M6,Outcome_M7,Outcome_M8,Outcome_M9,Outcome_M10,Outcome_M11,Outcome_M12", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "osales", "Multivariate regression data set from: https:\/\/link.springer.com\/article\/10.1007%2Fs10994-016-5546-z : This is a pre-processed version of the dataset used in Kaggles Online Product Sales competition (Kaggle 2012) that concerns the prediction of the online sales of consumer products. Each row in the dataset corresponds to a different product that is described by various product features as well as features of an advertising campaign. 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