smoteenn python

SmoteENN is a combination of two popular imbalanced data techniques, SMOTE (Synthetic Minority Over-sampling Technique) and Edited Nearest Neighbors. It is used for handling imbalanced datasets in Python.

To use SmoteENN in Python, you can follow these steps:

1. Install the required libraries:

   pip install imbalanced-learn

2. Import the necessary modules:

   from imblearn.combine import SMOTEENN
   from sklearn.datasets import make_classification
   from sklearn.model_selection import train_test_split

3. Generate a synthetic imbalanced dataset:

   X, y = make_classification(
       n_classes=2,
       class_sep=2,
       weights=[0.1, 0.9],
       n_informative=3,
       n_redundant=1,
       flip_y=0,
       n_features=20,
       n_clusters_per_class=1,
       n_samples=1000,
       random_state=10,
   )

4. Split the dataset into train and test sets:

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. Apply SmoteENN to balance the dataset:

   smote_enn = SMOTEENN(random_state=0)
   X_resampled, y_resampled = smote_enn.fit_resample(X_train, y_train)

Now you have a balanced dataset (`X_resampled` and `y_resampled`) that you can use for further analysis or model training.

Note: Make sure to adjust the parameters and settings according to your specific use case and data.