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from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression

from sklearn.linear_model import LogisticRegression

from sklearn.linear_model import LogisticRegression

import pandas as pd

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)

train = train.drop('Unnamed: 0', axis=1)

train = train.fillna(train.mean())

train.columns

X = train.drop(['satisfaction'], axis=1)
y = train['satisfaction']

from sklearn.linear_model import LogisticRegression
clf_lr = LogisticRegression(solver='liblinear')
clf_lr.fit(X, y)