Exploring the traditional way to catch fraud

In this exercise you're going to try finding fraud cases in our credit card dataset the "old way". First you'll define threshold values using common statistics, to split fraud and non-fraud. Then, use those thresholds on your features to detect fraud. This is common practice within fraud analytics teams.

Statistical thresholds are often determined by looking at the mean values of observations. Let's start this exercise by checking whether feature means differ between fraud and non-fraud cases. Then, you'll use that information to create common sense thresholds. Finally, you'll check how well this performs in fraud detection.

pandas has already been imported as pd.

This exercise is part of the course

Fraud Detection in Python

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Exercise instructions

Use groupby() to group df on Class and obtain the mean of the features.
Create the condition V1 smaller than -3, and V3 smaller than -5 as a condition to flag fraud cases.
As a measure of performance, use the crosstab function from pandas to compare our flagged fraud cases to actual fraud cases.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Get the mean for each group
____.____(____).mean()

# Implement a rule for stating which cases are flagged as fraud
df['flag_as_fraud'] = np.where(np.logical_and(______), 1, 0)

# Create a crosstab of flagged fraud cases versus the actual fraud cases
print(____(df.Class, df.flag_as_fraud, rownames=['Actual Fraud'], colnames=['Flagged Fraud']))

Edit and Run Code