Multiple Imputation Technique.py

from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

# Introduce some missing values in X
missing_mask = np.random.rand(*X.shape) < 0.1
X_missing = X.copy()
X_missing[missing_mask] = np.nan

imputer = IterativeImputer()
X_imputed = imputer.fit_transform(X_missing)