Autoencoder code implementation for credit card fraud detection

As usual, like all other projects, let's first load the data into an R dataframe and then perform EDA to understand the dataset better. Please note the inclusion of h2o as well as the doParallel library in the code. These inclusions enable us to use the AE that is part of the h2o library, as well as to utilize the multiple CPU cores that are present in the laptop/desktop as follows:

# including the required librarieslibrary(tidyverse)library(h2o)library(rio)library(doParallel)library(viridis)library(RColorBrewer)library(ggthemes)library(knitr)library(caret)library(caretEnsemble)library(plotly)library(lime)library(plotROC)library(pROC)

Initializing the H2O cluster in localhost ...

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