Hands-On Machine Learning for Cybersecurity

Approach

1. We are not going to perform feature engineering in the first instance. The dataset has been downgraded in order to contain 30 features (28 anonymized + time + amount).
2. We compare what happens when using resampling and when not using it. We test this approach using a simple logistic regression classifier.
3. We evaluate the models by using some of the performance metrics mentioned previously.
4. We repeat the best resampling/not-resampling method by tuning the parameters in the logistic-regression classifier.
5. We perform a classifications model using other classification algorithms.

Setting our input and target variables + resampling:

1. Normalize the Amount column
2. The Amount column is not in line with the anonymized features:

from sklearn.preprocessing ...