Data Retrieval

Description

This endpoint is to retrieve the dataset, and return a dictionary that lists sample size, information of categoric and numeric features in the dataset.

Request Body

NameDatatypeDescriptionMandatorySample valueNotes
clientIdStringIt is meant for identification purpose as information are stored under unique id valueYes
customersArray of DictionaryArray containing each customer’s informationThe fields are different per project, based on what we’re getting from the client’s DB.The array is mandatoryFor each customer’s information in the dictionary, the only mandatory field is customerID[ { "gender": "male", "age": 67, "personalIncome": 24299, "customerId": "0", "nationality": "US", "race": "American", "platformCountry": "US" }, { "gender": "female", "age": 20, "personalIncome": 7790, "customerId": "1", "nationality": "US", "race": "American", "platformCountry": "US" } . . . ]The set of key-value pair dictionary in the array object always changes. And the input validation of the set of key-value pair is defined and set by the customer

Response Body

NameDatatypeDescriptionSample valueNo. of decimal placesNotes
baseCategoricFeaturesArray of stringList of categorical features of each customer[ "race", "gender", "platformCountry", "nationality", "agentId" ]This is the enum for user to select the categoric features he is interest in
baseNumericFeaturesArray of stringList of numerical features of each customer[ "age", "personalIncome", "savingsRatio" ]This is the enum for user to select the numeric features he is interest in
categoricFeaturesInfoArray of dictionariesContains dataType, missing ,nunique and unique values for each categoric feature"categoricFeaturesInfo": [ { "info": { "dataType": "categoric", "missing": 0, "nunique": 2, "unique": [ "male", "female" ] }, "name": "gender" }, { "info": { "dataType": "categoric", "missing": 0, "nunique": 1, "unique": [ "US" ] }, "name": "nationality" } ]
dataTypeStringType of featurecategoric
missingIntegerNumber of missing data for particular feature0
nuniqueIntegerNumber of unique values2
uniqueArrayList of unique value[“female”, “male”]
numericFeaturesInfoArray of dictionariesContains information for each numeric feature"numericFeaturesInfo": [ { "info": { "25Percentile": 31, "50Percentile": 46, "75Percentile": 62, "dataType": "numeric", "kurtosis": -0.50211, "max": 100, "mean": 49.48949, "min": 19, "missing": 0.0, "nunique": 82.0, "skewness": 0.62364, "standardDeviation": 21.89702 }, "name": "age" }, { "info": { "25Percentile": 2807.5, "50Percentile": 5923, "75Percentile": 10727, "dataType": "numeric", "kurtosis": 2.30218, "max": 35101, "mean": 8077.2092, "min": 8, "missing": 0, "nunique": 976, "skewness": 1.56952, "standardDeviation": 7381.92973 }, "name": "personalIncome"} ]Contains "25percentile", "50percentile", "75percentile", "dataType", "kurtosis", "max", "mean", "min", "missing", "nunique", "skewness", "standardDeviation".
25PercentileFloat305
50PercentileFloat425
75PercentileFloat555
dataTypeStringNumeric
kurtosisFloat1.121215
maxFloat705
meanFloat42.56465
minFloat185
missingInteger00
nuniqueInteger530
skewnessFloat0.071565
standardDeviationFloat14.195415
numberOfCategoricFeaturesIntegerTotal number of categorical features for each customer50
numberOfNumericFeaturesIntegerTotal number of categorical features for each customer30
sampleSizeIntegerThe Length of the "customers” array50000
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