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from sklearn.model_selection import train_test_split,cross_val_predict,StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report,roc_auc_score,roc_curve
from imblearn.pipeline import Pipeline as imbPipe
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC, SVC
from sklearn.neighbors import KNeighborsClassifier



X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,
                                                    shuffle=True, stratify  = y)

dct = DecisionTreeClassifier(random_state=42)
sgd = SGDClassifier(random_state=42)
log = LogisticRegression(random_state=42)
svm_rbf = SVC(kernel="rbf", random_state=42)
svm_lin = LinearSVC(loss="hinge")
knn=KNeighborsClassifier()

kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=42)


Voting_pipeline = imbPipe([
    
    ("scaler", StandardScaler()),
    ("smote", SMOTE(random_state=42,n_jobs=-1)),
    ("voting", VotingClassifier(estimators=[("dct", dct),
                                            ("sgd", sgd),
                                            ("svm_rbf", svm_rbf),
                                            ("smv_lin", svm_lin),
                                            ("knn",knn),
                                            ("log", log)],voting="hard",n_jobs=-1))
])


y_pred = cross_val_predict(Voting_pipeline, X_train, y_train, cv = kfold)
print(classification_report(y_train, y_pred))
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Wed Jan 27 2021 07:48:39 GMT+0000 (Coordinated Universal Time)

#models #knn #svc #decisiontree #tree #logistics #sgd #classification #standarscaler

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