Add Mobile Botnet trained model

WANHEDA_Server/MobileBotNet/mobileBotNetModel.py

+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.impute import SimpleImputer
+
+# Importing the dataset
+dataset = pd.read_csv("/Users/harithachanuka/Documents/SLIIT/Research/Harry/Wanheda_Server/MobileBotNet/packet_data.csv", encoding='latin-1')
+X = dataset.iloc[:, [1,3]].values
+y = dataset.iloc[:, 5].values
+
+# Encoding categorical data-ForX
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X_S = LabelEncoder()
+X[:, 0] = labelencoder_X_S.fit_transform(X[:, 0])
+labelencoder_X_P = LabelEncoder()
+X[:, 1] = labelencoder_X_P.fit_transform(X[:, 1])
+
+labelencoder_y = LabelEncoder()
+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)
+
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_train = sc_X.fit_transform(X_train)
+X_test = sc_X.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)
+
+# Create confusion metrics to check the performance of algorithm
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)