testing algorithms.py

  
    # logistic regression -->
    lgr = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
    lgr.fit(X_train,y_train)

    # Support Vector Classifier -->
    svc = make_pipeline(StandardScaler(), SVC(random_state=0, gamma='auto'))
    svc.fit(X_train,y_train)
    
    # k-nearest neighbours -->
    knn = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=5))
    knn.fit(X_train,y_train)
    
    # Random Forest -->
    rft = make_pipeline(StandardScaler(), RandomForestClassifier(max_depth=2, random_state=0))
    rft.fit(X_train,y_train)

    # accuracy --> 
    accuracy_lgr = lgr.score(X_test,y_test)
    accuracy_svc = svc.score(X_test,y_test)
    accuracy_knn = knn.score(X_test,y_test)
    accuracy_rft = rft.score(X_test,y_test)
   
    # print out results -->
    print('Logistic Regression: {}'.format(accuracy_lgr))
    print('Support Vector Classifier: {}'.format(accuracy_svc))
    print('Knearest neighbors: {}'.format(accuracy_knn))
    print('Random Forests: {}'.format(accuracy_rft))