LoR.py

from sklearn.linear_model import LogisticRegression
lr_classifier = LogisticRegression(random_state=0)lr_classifier.fit(X_train, y_train)
lr_y_pred = lr_classifier.predict(X_test)
from sklearn.metrics import accuracy_score, precision_score, recall_score
score1 = accuracy_score(y_test, lr_y_pred)
score2 = precision_score(y_test, lr_y_pred)
score3 = recall_score(y_test, lr_y_pred)
print("---- Scores ----")
print("Accuracy score is: {}%".format(round(score1*100,2)))
print("Precision score is: {}".format(round(score2,2)))
print("Recall score is: {}".format(round(score3,2)))

best_accuracy = 0.0
c_val = 0.0
for i in np.arange(0.1,1.1,0.1):
  temp_classifier = LogisticRegression(C=i, random_state=0)
  temp_classifier.fit(X_train, y_train)
  temp_y_pred = temp_classifier.predict(X_test)
  score = accuracy_score(y_test, temp_y_pred)
  print("Accuracy score for C={} is: {}%".format(round(i,1), round(score*100,2)))
  if score>best_accuracy:
    best_accuracy = score
c_val = I
print('--------------------------------------------')
print('The best accuracy is {}% with C value as {}'.format(round(best_accuracy*100, 2), round(c_val,1)))