from sklearn.dummy import DummyClassifier
dummy_model = DummyClassifier(strategy = 'most_frequent', random_state = 0)
dummy_model.fit(x_train, y_train)

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

from sklearn.linear_model import LogisticRegression
logit_model = LogisticRegression(C=0.05, random_state=18, class_weight='balanced', penalty='l1')
logit_model.fit(x_train, y_train)

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

logit_model_base = LogisticRegression(random_state = 18)
from sklearn.model_selection import GridSearchCV
parameters = {'C': [0.03, 0.05, 0.08, 0.1, 0.3, 0.5, 10], 'penalty': ['l1', 'l2']}
logit_model_best = GridSearchCV(logit_model_base, param_grid = parameters, cv = 3)
logit_model_best.fit(x_train, y_train)

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993

from sklearn.linear_model import LogisticRegressionCV
logit_model_cv = LogisticRegressionCV(cv = 10, class_weight = 'balanced')
logit_model_cv.fit(x_train, y_train)

[[2831 3384 3485]

 [  36  104   71]

 [   9   28   45]]

precision    recall  f1-score   support

      1       0.98      0.29      0.45      9700
      2       0.03      0.49      0.06       211
      3       0.01      0.55      0.02        82

avg / total       0.96      0.30      0.44      9993

from sklearn.linear_model import LogisticRegressionCV
logit_model_cv = LogisticRegressionCV(cv = 10)
logit_model_cv.fit(x_train, y_train)

[[9700    0    0]

 [ 211    0    0]

 [  82    0    0]]

precision    recall  f1-score   support

      1       0.97      1.00      0.99      9700
      2       0.00      0.00      0.00       211
      3       0.00      0.00      0.00        82

avg / total       0.94      0.97      0.96      9993