Objective Our goal is to train a Neural Network to detect fraudulent credit card transactions in a dataset referring to two days transactions by european cardholders. Source: https://www.kaggle.com/mlg-ulb/creditcardfraud/data Data credit = read.csv(path) The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days. As we can see, this dataset consists of thirty explanatory variables, and a response variable which represents whether a transation was a fraud or not.
Data Frame This format is usually used when the information is not contained in just one dimension (vector) Example product <- c("Product A", "Product B", "Product C", "Product D", "Product E") price <- c(5, 15, 4, 6, 8) table_price_product <- data.frame(product, price) table_price_product ## product price ## 1 Product A 5 ## 2 Product B 15 ## 3 Product C 4 ## 4 Product D 6 ## 5 Product E 8 Indexing Access the D Product in the Products Table: