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from sklearn.impute import KNNImputer
# Select numerical features
numerical_features = df.select_dtypes(include=['int64', 'float64'])

# Initialize KNNImputer for numerical features
imputer_num = KNNImputer(n_neighbors=5)  # Set the desired value of k

# Impute missing values for numerical features
df[numerical_features.columns] = imputer_num.fit_transform(numerical_features)