Replace IT17106702.py

IT17106702.py

@@ -9,26 +9,12 @@ dataset = pd.read_csv("/Users/harithachanuka/Documents/SLIIT/Research/Harry/Wanh
 X = dataset.iloc[:, [1,3]].values
 y = dataset.iloc[:, 5].values
 
-# datasetnew = pd.read_csv("/Users/harithachanuka/Documents/SLIIT/Research/Implementation/new.csv", encoding='latin-1')
-# Xnw = datasetnew.iloc[:, :5].values
-
-
-#Encoding categorical data-ForX
+# Encoding categorical data-ForX
 from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-
 labelencoder_X_S = LabelEncoder()
 X[:, 0] = labelencoder_X_S.fit_transform(X[:, 0])
-#labelencoder_X_D = LabelEncoder()
-# X[:, 2] = labelencoder_X_D.fit_transform(X[:, 2])
 labelencoder_X_P = LabelEncoder()
 X[:, 1] = labelencoder_X_P.fit_transform(X[:, 1])
-# ct = ColumnTransformer([("Source", OneHotEncoder(), [1])], remainder = 'passthrough')
-# X = ct.fit_transform(X).toarray()
-# ct2 = ColumnTransformer([("Destination", OneHotEncoder(), [72])], remainder = 'passthrough')
-# X = ct2.fit_transform(X)
-# ct3 = ColumnTransformer([("Protocol", OneHotEncoder(), [118])], remainder = 'passthrough')
-# X = ct3.fit_transform(X)
 
 labelencoder_y = LabelEncoder()
 y = labelencoder_y.fit_transform(y)
@@ -36,10 +22,7 @@ y = labelencoder_y.fit_transform(y)
 # Splitting the dataset into the Training set and Test set
 from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
-# print(X_train)
-# print(X_test)
-# print(y_train)
-# print(X_train)
+
 
 # Feature Scaling
 from sklearn.preprocessing import StandardScaler
@@ -55,8 +38,6 @@ classifier.fit(X_train, y_train)
 # Predicting the Test set results
 y_pred = classifier.predict(X_test)
 
-
+# Create confusion metrics to check the performance of algorithm
 from sklearn.metrics import confusion_matrix
 cm = confusion_matrix(y_test, y_pred)
-
-#
\ No newline at end of file