{ "data_id": "43383", "name": "Superstore-Sales-Dataset", "exact_name": "Superstore-Sales-Dataset", "version": 1, "version_label": "v1.0", "description": "Context\nRetail dataset of a global superstore for 4 years.\nPerform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!\n\nContent\nTime series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail sales forecasting.\n\nDataset\nThe dataset is easy to understand and is self-explanatory\n\nInspiration\nPerform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!", "format": "arff", "uploader": "Onur Yildirim", "uploader_id": 30126, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 12:52:05", "update_comment": null, "last_update": "2022-03-23 12:52:05", "licence": "GPL 2", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102208\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Superstore-Sales-Dataset", "Context Retail dataset of a global superstore for 4 years. Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset! Content Time series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail s " ], "weight": 5 }, "qualities": { "NumberOfInstances": 9800, "NumberOfFeatures": 18, "NumberOfClasses": null, "NumberOfMissingValues": 11, "NumberOfInstancesWithMissingValues": 11, "NumberOfNumericFeatures": 3, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.001836734693877551, "PercentageOfNumericFeatures": 16.666666666666664, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0.11224489795918367, "AutoCorrelation": null, "PercentageOfMissingValues": 0.006235827664399093 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "Row_ID", "index": "0", "type": "numeric", "distinct": "9800", "missing": "0", "min": "1", "max": "9800", "mean": "4901", "stdev": "2829" }, { "name": "Order_ID", "index": "1", "type": "string", "distinct": "4922", "missing": "0" }, { "name": "Order_Date", "index": "2", "type": "string", "distinct": "1230", "missing": "0" }, { "name": "Ship_Date", "index": "3", "type": "string", "distinct": "1326", "missing": "0" }, { "name": "Ship_Mode", "index": "4", "type": "string", "distinct": "4", "missing": "0" }, { "name": "Customer_ID", "index": "5", "type": "string", "distinct": "793", "missing": "0" }, { "name": "Customer_Name", "index": "6", "type": "string", "distinct": "793", "missing": "0" }, { "name": "Segment", "index": "7", "type": "string", "distinct": "3", "missing": "0" }, { "name": "Country", "index": "8", "type": "string", "distinct": "1", "missing": "0" }, { "name": "City", "index": "9", "type": "string", "distinct": "529", "missing": "0" }, { "name": "State", "index": "10", "type": "string", "distinct": "49", "missing": "0" }, { "name": "Postal_Code", "index": "11", "type": "numeric", "distinct": "626", "missing": "11", "min": "1040", "max": "99301", "mean": "55273", "stdev": "32041" }, { "name": "Region", "index": "12", "type": "string", "distinct": "4", "missing": "0" }, { "name": "Product_ID", "index": "13", "type": "string", "distinct": "1861", "missing": "0" }, { "name": "Category", "index": "14", "type": "string", "distinct": "3", "missing": "0" }, { "name": "Sub-Category", "index": "15", "type": "string", "distinct": "17", "missing": "0" }, { "name": "Product_Name", "index": "16", "type": "string", "distinct": "1849", "missing": "0" }, { "name": "Sales", "index": "17", "type": "numeric", "distinct": "5757", "missing": "0", "min": "0", "max": "22638", "mean": "231", "stdev": "627" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }