{
    "data_id": "43609",
    "name": "SigmaCabPrediction",
    "exact_name": "SigmaCabPrediction",
    "version": 1,
    "version_label": "v1.0",
    "description": "Problem Statement\nWelcome to Sigma Cab Private Limited - a cab aggregator service. Their customers can download their app on smartphones and book a cab from any where in the cities they operate in. They, in turn search for cabs from various service providers and provide the best option to their client across available options. They have been in operation for little less than a year now. During this period, they have captured surgepricingtype from the service providers. \nYou have been hired by Sigma Cabs as a Data Scientist and have been asked to build a predictive model, which could help them in predicting the surgepricingtype pro-actively. This would in turn help them in matching the right cabs with the right customers quickly and efficiently. \nData\nVariable Definition\nTripID - ID for TRIP (Can not be used for purposes of modelling)\nTripDistance - The distance for the trip requested by the customer\nTypeofCab - Category of the cab requested by the customer\nCustomerSinceMonths - Customer using cab services since n months; 0 month means current month\nLifeStyleIndex - Proprietary index created by Sigma Cabs showing lifestyle of the customer based on their behaviour\nConfidenceLifeStyleIndex - Category showing confidence on the index mentioned above\nDestinationType - Sigma Cabs divides any destination in one of the 14 categories\nCustomerRating - Average of life time ratings of the customer till date\nCancellationLast1Month - Number of trips cancelled by the customer in last 1 month\nVar1, Var2 and Var3 - Continuous variables masked by the company. Can be used for modelling purposes\nGender - Gender of the customer\nSurgePricing_Type - Predictor variable can be of 3 types",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-24 00:39:52",
    "update_comment": null,
    "last_update": "2022-03-24 00:39:52",
    "licence": "CC BY-NC-SA 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102434\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "SigmaCabPrediction",
            "Problem Statement Welcome to Sigma Cab Private Limited - a cab aggregator service. Their customers can download their app on smartphones and book a cab from any where in the cities they operate in. They, in turn search for cabs from various service providers and provide the best option to their client across available options. They have been in operation for little less than a year now. During this period, they have captured surgepricingtype from the service providers. You have been hired by Sig "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 131662,
        "NumberOfFeatures": 14,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 137546,
        "NumberOfInstancesWithMissingValues": 90054,
        "NumberOfNumericFeatures": 9,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.00010633288268444958,
        "PercentageOfNumericFeatures": 64.28571428571429,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 68.39786726618159,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 7.462072796793522
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        }
    ],
    "features": [
        {
            "name": "Trip_ID",
            "index": "0",
            "type": "string",
            "distinct": "131662",
            "missing": "0"
        },
        {
            "name": "Trip_Distance",
            "index": "1",
            "type": "numeric",
            "distinct": "10326",
            "missing": "0",
            "min": "0",
            "max": "109",
            "mean": "44",
            "stdev": "26"
        },
        {
            "name": "Type_of_Cab",
            "index": "2",
            "type": "string",
            "distinct": "5",
            "missing": "20210"
        },
        {
            "name": "Customer_Since_Months",
            "index": "3",
            "type": "numeric",
            "distinct": "11",
            "missing": "5920",
            "min": "0",
            "max": "10",
            "mean": "6",
            "stdev": "4"
        },
        {
            "name": "Life_Style_Index",
            "index": "4",
            "type": "numeric",
            "distinct": "55978",
            "missing": "20193",
            "min": "2",
            "max": "5",
            "mean": "3",
            "stdev": "0"
        },
        {
            "name": "Confidence_Life_Style_Index",
            "index": "5",
            "type": "string",
            "distinct": "3",
            "missing": "20193"
        },
        {
            "name": "Destination_Type",
            "index": "6",
            "type": "string",
            "distinct": "14",
            "missing": "0"
        },
        {
            "name": "Customer_Rating",
            "index": "7",
            "type": "numeric",
            "distinct": "3931",
            "missing": "0",
            "min": "0",
            "max": "5",
            "mean": "3",
            "stdev": "1"
        },
        {
            "name": "Cancellation_Last_1Month",
            "index": "8",
            "type": "numeric",
            "distinct": "9",
            "missing": "0",
            "min": "0",
            "max": "8",
            "mean": "1",
            "stdev": "1"
        },
        {
            "name": "Var1",
            "index": "9",
            "type": "numeric",
            "distinct": "122",
            "missing": "71030",
            "min": "30",
            "max": "210",
            "mean": "64",
            "stdev": "22"
        },
        {
            "name": "Var2",
            "index": "10",
            "type": "numeric",
            "distinct": "58",
            "missing": "0",
            "min": "40",
            "max": "124",
            "mean": "51",
            "stdev": "5"
        },
        {
            "name": "Var3",
            "index": "11",
            "type": "numeric",
            "distinct": "96",
            "missing": "0",
            "min": "52",
            "max": "206",
            "mean": "75",
            "stdev": "12"
        },
        {
            "name": "Gender",
            "index": "12",
            "type": "string",
            "distinct": "2",
            "missing": "0"
        },
        {
            "name": "Surge_Pricing_Type",
            "index": "13",
            "type": "numeric",
            "distinct": "3",
            "missing": "0",
            "min": "1",
            "max": "3",
            "mean": "2",
            "stdev": "1"
        }
    ],
    "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
}