TypeError: float() argument must be a string or a number, not ‘LabelEncoder’

Still working on my Logistic Regression problem and as I trained my model, error appears;

TypeError: float() argument must be a string or a number, not ‘LabelEncoder’

This is my code;

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
subscription.poutcome = le.fit(subscription.poutcome)
subscription.day_of_week= le.fit(subscription.day_of_week)
subscription.month = le.fit(subscription.month)

#splitting the dataset for training
X = subscription.drop(['y'],axis=1)
y = subscription['y']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train,y_test = train_test_split(X, y, test_size = 0.2, random_state =10)

#fit model
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
TypeError: float() argument must be a string or a number, not 'LabelEncoder'

The main reason this happened is cause the values in the columns were fit to the label encoder and not transformed.

The right way to have done this was;

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
subscription.poutcome = le.fit_transform(subscription.poutcome)
subscription.day_of_week= le.fit_transform(subscription.day_of_week)
subscription.month = le.fit_transform(subscription.month)