Add random dataset and testing file for Naive Bayes

NaiveBayes/nb_testing.py

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+import numpy as np 
+from sklearn.model_selection import train_test_split
+from sklearn import datasets
+#sklearn for just generating random data
+import matplotlib.pyplot as plt 
+from Naive import NaiveBayes 
+#import Naive Bayes class from the file Naive
+
+#Accuracy function
+def accuracy(y_true, y_predicted):
+	acc = np.sum(y_true == y_predicted) / len(y_true)
+	print("Accuracy of given model is : " + str(acc * 100))
+	return 
+
+#I have create tested data here
+X, Y = datasets.make_classification(n_samples = 1000, n_features = 10, n_classes = 5, random_state = 121, n_informative = 5)
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 121)
+
+# You  can check random data through it
+print(X)
+print(Y)
+
+nb = NaiveBayes()
+np.fit(X_train, Y_train) # fitting test data
+final_predictions = np.predict(X_test) #final predicted values for testing data
+
+#finding accuracy after comparing through original classes through testing data
+accuracy(Y_test, final_predictions)
+
+