Handle Extreme Imbalance
Apply techniques to handle the extreme class imbalance in fraud data — undersampling, SMOTE, and cost-sensitive learning.
2
Handle Extreme Imbalance
python • Step 2
Apply techniques to handle the extreme class imbalance in fraud data — undersampling, SMOTE, and cost-sensitive learning.
smoteundersamplingclass_weightimbalanced_learning
Learning Objectives
- →Apply random undersampling and SMOTE
- →Use cost-sensitive learning with class weights
- →Compare strategies on evaluation metrics
Dataset
View on Kaggle Matches Kaggle
~284k transactions, PCA-anonymized features (V1–V28), severe class imbalance.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/creditcard.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named creditcard.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "creditcard.csv").
Dataset Columns
V1V2V3V14V17AmountClass- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
Original train: Fraud rate:
0/2
Export to GitHub
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