from imblearn.over_sampling import SMOTE

SMOTE (Synthetic Minority Over-sampling Technique) is a popular algorithm for dealing with imbalanced datasets. It works by creating synthetic samples of the minority class to balance it with the majority class. The imblearn library provides an implementation of SMOTE that can be used with scikit-learn.

To use SMOTE, you first need to install the imblearn library. You can do this using pip:

pip install imblearn

Once you have imblearn installed, you can import the SMOTE class and use it to oversample your dataset. Here's an example:

from imblearn.over_sampling import SMOTE

X_resampled, y_resampled = SMOTE().fit_resample(X, y)

In this example, `X` is your feature matrix and `y` is your target vector. `SMOTE().fit_resample(X, y)` returns a new feature matrix (`X_resampled`) and target vector (`y_resampled`) with the minority class oversampled.

It's important to note that SMOTE should only be used on the training data, not the testing data. You should also be careful not to oversample too much, as this can lead to overfitting.