Exploring your data
In the next exercises, you will be looking at bank payment transaction data. The financial transactions are categorized by type of expense, as well as the amount spent. Moreover, you have some client characteristics available such as age group and gender. Some of the transactions are labelled as fraud; you'll treat these labels as given and will use those to validate the results.
When using unsupervised learning techniques for fraud detection, you want to distinguish normal from abnormal (thus potentially fraudulent) behavior. As a fraud analyst to understand what is "normal", you need to have a good understanding of the data and its characteristics. Let's explore the data in this first exercise.
本练习是课程的一部分
Fraud Detection in Python
交互式实操练习
通过完成这段示例代码来试试这个练习。
# Get the dataframe shape
df.____
# Display the first 5 rows
df.____