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  3. Fraud Detection in Python

Exercise

Random Forest Classifier - part 1

Let's now create a first random forest classifier for fraud detection. Hopefully you can do better than the baseline accuracy you've just calculated, which was roughly 96%. This model will serve as the "baseline" model that you're going to try to improve in the upcoming exercises. Let's start first with splitting the data into a test and training set, and defining the Random Forest model. The data available are features X and labels y.

Instructions

100 XP
  • Import the random forest classifier from sklearn.
  • Split your features X and labels y into a training and test set. Set aside a test set of 30%.
  • Assign the random forest classifier to model and keep random_state at 5. We need to set a random state here in order to be able to compare results across different models.