Delete volume.py

volume.py

-# Importing the libraries
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-
-# Importing the dataset
-dataset = pd.read_csv('packet_data.csv')
-X = dataset.iloc[:, :-1] .values
-y = dataset.iloc[:, 5].values
-
-#Encoding categorical data
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-labelencoder_X_T = LabelEncoder()
-X[:, 0] = labelencoder_X_T.fit_transform(X[:, 0])
-labelencoder_X_S = LabelEncoder()
-X[:, 1] = labelencoder_X_S.fit_transform(X[:, 1])
-labelencoder_X_D = LabelEncoder()
-X[:, 2] = labelencoder_X_D.fit_transform(X[:, 2])
-labelencoder_X_P = LabelEncoder()
-X[:, 3] = labelencoder_X_P.fit_transform(X[:, 3])
-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)
-
-#Splitting the data set 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)
-
-#Feature scaling
-from sklearn.preprocessing import StandardScaler
-sc_X = StandardScaler()
-X_train = sc_X.fit_transform(X_train)
-X_test = sc_X.fit_transform(X_test)
-
-# Fitting classifier to the Training set
-from sklearn.naive_bayes import GaussianNB
-classifier = GaussianNB()
-classifier.fit(X_train, y_train)
-
-# Predicting the Test set results
-y_pred = classifier.predict(X_test)
-
-# Making the Confusion Matrix
-from sklearn.metrics import confusion_matrix
-cm = confusion_matrix(y_test, y_pred)
-
-# Visualising the Training set results
-from matplotlib.colors import ListedColormap
-X_set, y_set = X_train, y_train
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
-plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
-plt.xlim(X1.min(), X1.max())
-plt.ylim(X2.min(), X2.max())
-for i, j in enumerate(np.unique(y_set)):
-    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
-plt.title('BotNet (Training set)')
-plt.xlabel('Time Source Dest & Protocol')
-plt.ylabel('Malicious Or Not')
-plt.legend()
-plt.show()
-
-# Visualising the Test set results
-from matplotlib.colors import ListedColormap
-X_set, y_set = X_test, y_test
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
-                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
-plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
-plt.xlim(X1.min(), X1.max())
-plt.ylim(X2.min(), X2.max())
-for i, j in enumerate(np.unique(y_set)):
-    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
-                c = ListedColormap(('red', 'green'))(i), label = j)
-plt.title('KNN (Test set)')
-plt.xlabel('Age')
-plt.ylabel('Estimated Salary')
-plt.legend()
-plt.show()