{ "data_id": "43741", "name": "Predict-Amazon-Stock-Price-tomorrow", "exact_name": "Predict-Amazon-Stock-Price-tomorrow", "version": 1, "version_label": "v1.0", "description": "I have created this dataset to showcase the use of predictive modeling using the stock market as a case study. This dataset is designed to help and predict tomorrow's Amazon stock price. If you want to get the most updated dataset you will need to pull them in real time. I have shared my code to pull data using Yahoo Finance API and preprocess it in Data Analytics for Fun Github Repository\nThe uploaded dataset is for Jan 11, 2021. \nWhat are the columns?\nyes_changeP: Yesterday Amazon's stock price change\nlastweek_changeP: Last week Amazon's stock price change\ndow_yes_changeP: Yesterday Dow Jones change\ndow_lastweek_changeP: Last Week Dow Jones change\nnasdaq_yes_changeP: Yesterday NASDAQ 100 change\nnasdaq_lastweek_changeP: Last Week NASDAQ 100 change\ntoday_changeP: Today Amazon's stock price change\nTo learn more about the dataset and see a very simple prediction model applied to the dataset you may watch this YouTube Video where I have explained the dataset and also prediction: A Taste for Prediction: Predict Tomorrow's Amazon Stock Price", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-24 07:45:59", "update_comment": null, "last_update": "2022-03-24 07:45:59", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102566\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Predict-Amazon-Stock-Price-tomorrow", "I have created this dataset to showcase the use of predictive modeling using the stock market as a case study. This dataset is designed to help and predict tomorrow's Amazon stock price. If you want to get the most updated dataset you will need to pull them in real time. I have shared my code to pull data using Yahoo Finance API and preprocess it in Data Analytics for Fun Github Repository The uploaded dataset is for Jan 11, 2021. What are the columns? yes_changeP: Yesterday Amazon's stock price " ], "weight": 5 }, "qualities": { "NumberOfInstances": 350, "NumberOfFeatures": 8, "NumberOfClasses": null, "NumberOfMissingValues": 1, "NumberOfInstancesWithMissingValues": 1, "NumberOfNumericFeatures": 7, "NumberOfSymbolicFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0.2857142857142857, "AutoCorrelation": null, "PercentageOfMissingValues": 0.03571428571428571, "Dimensionality": 0.022857142857142857, "PercentageOfNumericFeatures": 87.5, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0 }, "tags": [ { "tag": "Transportation", "uploader": "38960" } ], "features": [ { "name": "t", "index": "0", "type": "string", "distinct": "350", "missing": "0" }, { "name": "yes_changeP", "index": "1", "type": "numeric", "distinct": "350", "missing": "0", "min": "-8", "max": "8", "mean": "0", "stdev": "2" }, { "name": "lastweek_changeP", "index": "2", "type": "numeric", "distinct": "350", "missing": "0", "min": "-13", "max": "21", "mean": "1", "stdev": "5" }, { "name": "dow_yes_changeP", "index": "3", "type": "numeric", "distinct": "350", "missing": "0", "min": "-13", "max": "11", "mean": "0", "stdev": "2" }, { "name": "dow_lastweek_changeP", "index": "4", "type": "numeric", "distinct": "350", "missing": "0", "min": "-23", "max": "14", "mean": "0", "stdev": "4" }, { "name": "nasdaq_yes_changeP", "index": "5", "type": "numeric", "distinct": "350", "missing": "0", "min": "-12", "max": "10", "mean": "0", "stdev": "2" }, { "name": "nasdaq_lastweek_changeP", "index": "6", "type": "numeric", "distinct": "350", "missing": "0", "min": "-19", "max": "14", "mean": "1", "stdev": "4" }, { "name": "today_changeP", "index": "7", "type": "numeric", "distinct": "349", "missing": "1", "min": "-8", "max": "8", "mean": "0", "stdev": "2" } ], "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 }