Using statistics to define normal behavior
In the previous exercises we saw that fraud is more prevalent in certain transaction categories, but that there is no obvious way to segment our data into for example age groups. This time, let's investigate the average amounts spend in normal transactions versus fraud transactions. This gives you an idea of how fraudulent transactions differ structurally from normal transactions.
This exercise is part of the course
Fraud Detection in Python
Exercise instructions
- Create two new dataframes from fraud and non-fraud observations. Locate the data in
dfwith.locand assign the condition "where fraud is 1" and "where fraud is 0" for creation of the new dataframes. - Plot the
amountcolumn of the newly created dataframes in the histogram plot functions and assign the labelsfraudandnonfraudrespectively to the plots.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create two dataframes with fraud and non-fraud data
df_fraud = df.____[df.____ == ____]
df_non_fraud = df.____[df.____ == ____]
# Plot histograms of the amounts in fraud and non-fraud data
plt.hist(____.____, alpha=0.5, label='____')
plt.hist(____.____, alpha=0.5, label='____')
plt.legend()
plt.show()