diff --git a/3217-classification-lr-example5.py b/3217-classification-lr-example5.py
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index fa7506699351e8ba3e9036de4caf39cb72018eb9..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example5.py
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-#Import scikit-learn dataset library
-from sklearn import datasets
-from sklearn.model_selection import train_test_split
-from sklearn import svm, metrics
-
-
-
-#Load dataset
-cancer = datasets.load_breast_cancer()
-
-# print the names of the  features
-print("Features: ", cancer.feature_names)
-
-# print the label type of cancer('malignant' 'benign')
-print("Labels: ", cancer.target_names)
-
-# print data(feature)shape
-print (cancer.data.shape)
-
-
-# Split dataset into training set and test set
-X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2) # 70% training and 30% test
-
-
-#Create a svm Classifier
-clf = svm.SVC(kernel='linear') # Linear Kernel
-
-#Train the model using the training sets
-clf.fit(X_train, y_train)
-
-#Predict the response for test dataset
-y_pred = clf.predict(X_test)
-
-print("Accuracy:",metrics.accuracy_score(y_test, y_pred))