machine learning projects in python

1from sklearn.datasets import load_wine
2from sklearn.naive_bayes import GaussianNB
3from sklearn.svm import SVC
4from sklearn.ensemble import StackingClassifier, RandomForestClassifier
5from sklearn.model_selection import StratifiedShuffleSplit
6random_state = 42
7
8X,y = load_wine(return_X_y = True)
9mean, std = X.mean(axis = 0), X.std(axis = 0)
10X = (X - mean)/std
11def make_models():
12  models = []
13  models.append(('rfc', RandomForestClassifier(random_state = random_state, n_estimators = 20)))
14  models.append(('svm', SVC(C = 0.01)))
15  models.append(('gnb', GaussianNB()))
16  return models
17
18models = make_models()
19clf = StackingClassifier(estimators = models, final_estimator = RandomForestClassifier(random_state = random_state),)
20
21scores = []
22i = 0
23for indices in kfold.split(X, y):
24  train_indices, test_indices = indices
25  X_train, X_test = X[train_indices], X[test_indices] # Task 2 split the data set with StratifiedKFold
26  y_train, y_test = y[train_indices], y[test_indices]
27  clf.fit(X_train, y_train)
28  curr_score = clf.score(X_test, y_test)
29  i+=1
30  print(f'{i}- Fold #{i} accuracy = ', str(round(curr_score,4)*100) + '%') # Task 4 dispaly the score
31  scores.append(curr_score)
32print(10*'--')
33print('The mean of  scores is: ', str(round(sum(scores)/k,4)*100) + '%') #Task 4 display the mean of scors
34
35#for more projects:
36#https://github.com/MohammedAlbaqerH
37