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Cursos Gerais ·
Estrutura de Dados
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Preview text
Python technical tasks 1 Create a new column named chargeback based on whether the disputeddate column is populated or not The results should be 1 for disputeddate populated and 0 for disputeddate blank 2 Keep only the digits in the orderid column PS Keep in mind that this function should work in case the number of digits in the orderid column increase over time 3 Create a new column with the time difference between the columns createddatetime and disputeddate Business Case Analysis We have provided you with a sample dataset containing transactions from a specific period The column chargeback which you have created identifies whether we have received an indication of fraudulent activity Your objective is to identify patterns that can accurately distinguish between fraudulent and nonfraudulent transactions These defined patterns will be instrumental in our efforts to proactively prevent fraudulent behavior in future transactions In case of any questions please feel free to reach out to us Data info Transaction Status Status of the Transaction ID as indicated by the Payment Provider Available statuses Settled the transaction was completed successfully Failed error when sending the transaction to the processor Authorization Expired the transaction spent too much time being authorized and was not settled Voided the transaction was cancelled by the user Gateway Rejected the transaction was rejected at the payment gateway level due to being flagged by risk related checks Processor Declined the payment processor bank has declined the transaction The reason for decline is described in the Processor Response Text column Order ID Unique identification number to the ride request PS One Order ID can have different Transaction IDs for the actual ride fare Gateway Rejection Reason Indicates if the transaction was rejected by the gateway due to being a possible duplicate for example if a customer refreshes the checkout page or clicks buy button multiple times generating new requests or a possible fraud if the transaction behavior was identified as a risk of fraud due to the rules set on gateway side
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Preview text
Python technical tasks 1 Create a new column named chargeback based on whether the disputeddate column is populated or not The results should be 1 for disputeddate populated and 0 for disputeddate blank 2 Keep only the digits in the orderid column PS Keep in mind that this function should work in case the number of digits in the orderid column increase over time 3 Create a new column with the time difference between the columns createddatetime and disputeddate Business Case Analysis We have provided you with a sample dataset containing transactions from a specific period The column chargeback which you have created identifies whether we have received an indication of fraudulent activity Your objective is to identify patterns that can accurately distinguish between fraudulent and nonfraudulent transactions These defined patterns will be instrumental in our efforts to proactively prevent fraudulent behavior in future transactions In case of any questions please feel free to reach out to us Data info Transaction Status Status of the Transaction ID as indicated by the Payment Provider Available statuses Settled the transaction was completed successfully Failed error when sending the transaction to the processor Authorization Expired the transaction spent too much time being authorized and was not settled Voided the transaction was cancelled by the user Gateway Rejected the transaction was rejected at the payment gateway level due to being flagged by risk related checks Processor Declined the payment processor bank has declined the transaction The reason for decline is described in the Processor Response Text column Order ID Unique identification number to the ride request PS One Order ID can have different Transaction IDs for the actual ride fare Gateway Rejection Reason Indicates if the transaction was rejected by the gateway due to being a possible duplicate for example if a customer refreshes the checkout page or clicks buy button multiple times generating new requests or a possible fraud if the transaction behavior was identified as a risk of fraud due to the rules set on gateway side