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  3. Fraud Detection in Python

Exercise

Applying SMOTE

In this exercise, you're going to re-balance our data using the Synthetic Minority Over-sampling Technique (SMOTE). Unlike ROS, SMOTE does not create exact copies of observations, but creates new, synthetic, samples that are quite similar to the existing observations in the minority class. SMOTE is therefore slightly more sophisticated than just copying observations, so let's apply SMOTE to our credit card data. The dataset df is available and the packages you need for SMOTE are imported. In the following exercise, you'll visualize the result and compare it to the original data, such that you can see the effect of applying SMOTE very clearly.

Instructions

100 XP
  • Use the prep_data function on df to create features X and labels y.
  • Define the resampling method as SMOTE of the regular kind, under the variable method.
  • Use .fit_resample() on the original X and y to obtain newly resampled data.
  • Plot the resampled data using the plot_data() function.