Delete IT17106702.py

IT17106702.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)