x_train = x_train.reshape(len(x_train),1)
y_train = train.loc[:,'Direction']
test = Weekly[Weekly['Year'] >= 2009]
x_test = test.iloc[:,3]
x_test = x_test.reshape(len(x_test),1)
y_test = test.loc[:,'Direction']
glm2 = LogisticRegression()
glm2.pred = glm2.fit(x_train, y_train).predict(x_test)
print(pd.DataFrame(confusion_matrix(y_test, glm2.pred), index=['y=0', 'y=1'], columns=['y_pred=0', 'y_pred=1']))
print('error rate: ', accuracy_score(y_test, glm2.pred)) # 62.5%
# (e)
lda = LinearDiscriminantAnalysis()
lda.pred = lda.fit(x_train, y_train).predict(x_test)
print(pd.DataFrame(confusion_matrix(y_test, lda.pred), index=['y=0', 'y=1'], columns=['y_pred=0', 'y_pred=1']))
print('error rate: ', accuracy_score(y_test, lda.pred)) # 62.5%
# (f)
qda = QuadraticDiscriminantAnalysis()
qda.pred = qda.fit(x_train, y_train).predict(x_test)
print(pd.DataFrame(confusion_matrix(y_test, qda.pred), index=['y=0', 'y=1'], columns=['y_pred=0', 'y_pred=1']))
print('error rate: ', accuracy_score(y_test, qda.pred)) # 58.7%
# (g)
knn = KNeighborsClassifier(n_neighbors=1)
knn.pred = knn.fit(x_train, y_train).predict(x_test)
print('error rate: ', accuracy_score(y_test, knn.pred)) # 49%
# (h): Logistic and LDA models are the best.
