Feature selection mutual_info_classif

#Import Libraries
import numpy as np
import pandas as pd
from sklearn.feature_selection import GenericUnivariateSelect, mutual_info_classif


# 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
genericunivariateselect = GenericUnivariateSelect(mutual_info_classif, mode='k_best', param=10)
transformed_data=genericunivariateselect.fit_transform(df, y)
genericunivariateselect.get_feature_names_out()