Feature Selection with regularized model

#Import Libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import SelectFromModel


# Data Processing
df=pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/00537/sobar-72.csv")
y=df.pop('ca_cervix')


#Feature Selection
model = LogisticRegression(penalty='l1',solver='liblinear')
regularization_based = SelectFromModel(estimator=model).fit(df, y)
transformed_data=regularization_based.transform(df)
regularization_based.get_feature_names_out()