Python for Finance Cookbook

How to do it...

Execute the following steps to investigate different approaches to handling class imbalance.

1. Import the libraries:

import pandas as pdimport seaborn as snsfrom imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYNfrom imblearn.under_sampling import RandomUnderSamplerfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom collections import Counterfrom chapter_9_utils import performance_evaluation_report

2. Load and prepare the data:

df = pd.read_csv('credit_card_fraud.csv')X = df.copy()y = X.pop('Class')RANDOM_STATE = 42X_train, X_test, y_train, y_test = train_test_split(X, y,                                                     test_size=0.2,                                                     stratify=y)

3. Train the baseline model:

rf = RandomForestClassifier(random_state=RANDOM_STATE) ...