{
    "data_id": "43486",
    "name": "Bike-Sharing-Washington-DC",
    "exact_name": "Bike-Sharing-Washington-DC",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nClimate change is forcing cities to re-imaging their transportation infrastructure. Shared mobility concepts, such as car sharing, bike sharing or scooter sharing become more and more popular. And if they are implemented well, they can actually contribute to mitigating climate change. Bike sharing in particular is interesting because no electricity of gasoline is necessary (unless e-bikes are used) for this mode of transportation. However, there are inherent problems to this type of shared mobility:\n\nvarying demand at bike sharing stations needs to be balanced to avoid oversupply or shortages\nheavily used bikes break down more often\n\nForecasting the future demand can help address those issues. Moreover, demand forecasts can help operators decide whether to expand the business, determine adequate prices and generate additional income through advertisements at particularly busy stations.\nBut that's not all. Another challenge is redistributing bikes between stations and determining the optimal routes. And determining the location of new stations is also an area of interest for operators.\nContent\nThis dataset can be used to forecast demand to avoid oversupply and shortages. It spans from January 1, 2011, until December 31, 2018. Determining new station locations, analyzing movement patterns or planning routes will only be possible with additional data.\n\ndate - date with the format yyyy-mm-dd\ntemp_avg - average daily temperature in degree Celsius\ntemp_min - minimum daily temperature in degree Celsius\ntemp_max - maximum daily temperature in degree Celsius\ntemp_observ - temperature at the time of observation in degree Celsius\nprecip - amount of precipitation in mm\nwind - wind speed in meters per second\nwt_fog - weather type fog, ice fog, or freezing fog (may include heavy fog)    \nwtheavyfog - weather type heavy fog or heaving freezing fog (not always distinguished from fog)    \nwt_thunder - weather type thunder\nwt_sleet - weather type ice pellets, sleet, snow pellets, or small hail\nwt_hail - weather type hail (may include small hail)\nwt_glaze - weather type glaze or rime\nwt_haze - weather type smoke or haze    \nwtdriftsnow - weather type blowing or drifting snow\nwthighwind - weather type high or damaging winds\nwt_mist - weather type mist\nwt_drizzle - weather type drizzle    \nwt_rain - weather type rain (may include freezing rain, drizzle, and freezing drizzle)\nwtfreezerain - weather type freezing rain    \nwt_snow - weather type snow, snow pellets, snow grains, or ice crystals    \nwtgroundfog - weather type ground fog\nwticefog -    weather type ice fog or freezing fog\nwtfreezedrizzle - weather type freezing drizzle\nwt_unknown - weather type unknown source of precipitation\ncasual - number of unregistered customers\nregistered - number of registered customers\ntotal_cust - sum of registered and casual customers\nholiday - indicates whether the day is a holiday or not\n\nAcknowledgements\nThe data I used to create this dataset was taken from:\n\nCapital Bikeshare for the bike sharing demand,\nNOAA's National Climatic Data Center for weather data,\nDC Department of Human Resources for data on public holidays.\n\nInspiration\nThink about the following questions\/topics and add more data to this dataset to improve your results:\n\nWhat will tomorrow's, next week's or next month's bike demand? Use time series analysis to determine this.\nUse anomaly detection to identify seasonality and trend in daily customers data.\nWhich features are particularly important for the forecast of the bike demand?",
    "format": "arff",
    "uploader": "Onur Yildirim",
    "uploader_id": 30126,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 13:27:14",
    "update_comment": null,
    "last_update": "2022-03-23 13:27:14",
    "licence": "CC BY-NC-SA 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102311\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Bike-Sharing-Washington-DC",
            "Context Climate change is forcing cities to re-imaging their transportation infrastructure. Shared mobility concepts, such as car sharing, bike sharing or scooter sharing become more and more popular. And if they are implemented well, they can actually contribute to mitigating climate change. Bike sharing in particular is interesting because no electricity of gasoline is necessary (unless e-bikes are used) for this mode of transportation. However, there are inherent problems to this type of shar "
        ],
        "weight": 5
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        "NumberOfFeatures": 29,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 51510,
        "NumberOfInstancesWithMissingValues": 2922,
        "NumberOfNumericFeatures": 28,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.009924709103353867,
        "PercentageOfNumericFeatures": 96.55172413793103,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
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        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 100,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 60.78736812292006
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        },
        {
            "uploader": "38960",
            "tag": "Statistics"
        }
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