Frequency feature for one account

A frequency feature counts how frequently a certain event has happened in the past. Creating such features helps detecting anomalous behavior. In the video, you learned how to create a frequency feature based on a categorical feature.

You're now provided with transactional data from Bob. One of the columns is called channel_cd which indicates the payment channel that Bob used to book each of his transactions. You'll be creating a frequency feature called freq_channel based on the column channel_cd using the function rollapply(). You can use ?rollaply in the console to see the function documentation.

The dataset trans_Bob, the zoo and dplyr packages are loaded in your workspace.

Cet exercice fait partie du cours

Fraud Detection in R

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Instructions

Write a function frequency_fun() which takes steps and channel as inputs, counts the number of steps, and sums how often the latest channel has been used in the past.
Create the feature freq_channel by using the function rollapply on the transfer_id column. The feature should count how often a particular channel_cd has been used before.
Print the features channel_cd, freq_channel and fraud_flag. Inspect the newly created feature.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Frequency feature based on channel_cd
frequency_fun <- function(steps, channel) {
  n <- ___(___)
  frequency <- ___(___[1:n] == ___[___])
  return(frequency)
}

# Create freq_channel feature
freq_channel <- ___(trans_Bob$___, width = list(-1:-length(trans_Bob$___)), partial = ___, FUN = ___, trans_Bob$___)

# Print the features channel_cd, freq_channel and fraud_flag next to each other
freq_channel <- c(0, freq_channel)
cbind.data.frame(trans_Bob$___, ___, trans_Bob$fraud_flag)

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