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.

Deze oefening maakt deel uit van de cursus

Fraud Detection in Python

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from imblearn.over_sampling import SMOTE

# Run the prep_data function
X, y = ____(df)

# Define the resampling method
method = ____()

# Create the resampled feature set
X_resampled, y_resampled = method.____(____, ____)

# Plot the resampled data
plot_data(____, ____)

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