{ "data_id": "43740", "name": "GOOG-Ticker-stock-data", "exact_name": "GOOG-Ticker-stock-data", "version": 1, "version_label": "v1.0", "description": "Context\nThe stock prices dataset of a ticker is a good start to slice and dice and good for forecasting of the stock prices.\nThe GOOG ticker data is taken\nContent\nDataset is comprising of the below columns and each row having the days data, an year data is pulled into csv file.\nDate Open High Low Close Volume\nAcknowledgements\nCredits: The data is pulled from the Google \nInspiration\nStock data for forecasting stock prices.", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-24 07:45:53", "update_comment": null, "last_update": "2022-03-24 07:45:53", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102565\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "GOOG-Ticker-stock-data", "Context The stock prices dataset of a ticker is a good start to slice and dice and good for forecasting of the stock prices. The GOOG ticker data is taken Content Dataset is comprising of the below columns and each row having the days data, an year data is pulled into csv file. Date Open High Low Close Volume Acknowledgements Credits: The data is pulled from the Google Inspiration Stock data for forecasting stock prices. " ], "weight": 5 }, "qualities": { "NumberOfInstances": 249, "NumberOfFeatures": 6, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 5, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.024096385542168676, "PercentageOfNumericFeatures": 83.33333333333334, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "AutoCorrelation": null, "PercentageOfMissingValues": 0 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "Date", "index": "0", "type": "string", "distinct": "249", "missing": "0" }, { "name": "Open", "index": "1", "type": "numeric", "distinct": "241", "missing": "0", "min": "671", "max": "838", "mean": "763", "stdev": "39" }, { "name": "High", "index": "2", "type": "numeric", "distinct": "246", "missing": "0", "min": "672", "max": "842", "mean": "767", "stdev": "38" }, { "name": "Low", "index": "3", "type": "numeric", "distinct": "245", "missing": "0", "min": "663", "max": "832", "mean": "758", "stdev": "39" }, { "name": "Close", "index": "4", "type": "numeric", "distinct": "246", "missing": "0", "min": "668", "max": "836", "mean": "763", "stdev": "39" }, { "name": "Volume", "index": "5", "type": "numeric", "distinct": "249", "missing": "0", "min": "587421", "max": "5939199", "mean": "1592365", "stdev": "702813" } ], "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 }