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  1. 3. Used to read dataset from CSV file
  2. 4. y, test_size=0.2) Used to split data into training and testing sets
  3. 5. = GaussianNB() Creates Naive Bayes model
  4. 9. y_train) Used to train the model
  5. 11. = LogisticRegression() Creates Logistic Regression model
  6. 12. = StandardScaler() Creates scaler object for feature scaling
Down
  1. 1. y_pred) Generates confusion matrix
  2. 2. y_pred) Calculates accuracy
  3. 6. y_pred) Calculates regression error
  4. 7. = LinearRegression() Creates Linear Regression model
  5. 8. = model.predict(X_test) Used to predict output
  6. 10. = sc.fit_transform(X) Applies feature scaling