How can I implement SMOTE in Python?

How can I implement SMOTE in Python?

You can use the imbalanced-learn library. First, install it using pip install imbalanced-learn. Then, you can use the SMOTE class to generate synthetic samples.

Can you show me a code example?

Sure! Here's a simple example:

from imblearn.over_sampling import SMOTE
import pandas as pd

# Sample data
X = pd.DataFrame({'feature1': [1, 2, 3, 4, 5], 'feature2': [5, 4, 3, 2, 1]})
y = pd.Series([0, 0, 1, 0, 1])

# Apply SMOTE
smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)

What does the fit_resample method do?

The fit_resample method fits the SMOTE algorithm to your data and generates synthetic samples to balance the classes.

Are there any parameters I can adjust in SMOTE?

Yes, you can adjust parameters like sampling_strategy, k_neighbors, and random_state to customize the behavior of SMOTE.