for i in range(len(test)):
y_test.append(test[i][0])
X_test.append(test[i][1:])
# Instantiate the Naive Bayes classifier model made from scratch
model = NaiveBayesClassifier()
# Fit the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Print the accuracy of the model
print("NaiveBayesClassifier accuracy: {0:.3f}".format(model.accuracy(y_test, y_pred)))
# Instantiate the Gaussian Naive Bayes classifier model from Scikit-learn
model = GaussianNB()
# Fit the model
model.fit(X_train, y_train)
# Make predictions
import pandas
from naive_bayes_classifier import NaiveBayesClassifier
df = pandas.read_csv("weather.csv", sep=";")
naive_bayes = NaiveBayesClassifier(df, 0.6)
naive_bayes.train_algorithm()
accuracy = naive_bayes.test_algorithm()
print("Algorithm Accuracy : " + str(accuracy) + " %")
tuple_data = ('Overcast', 'Hot', 'Normal', True)
prediction = naive_bayes.predict(tuple_data)
print("Prediction for " + str(tuple_data) + " is " + prediction)
NB_classifier = NaiveBayesClassifier()
spam_train = spam_data[:int(len(spam_data) * 2 / 3)]
spam_test = spam_data[int(len(spam_data) * 2 / 3):]
legit_train = legit_data[:int(len(legit_data) * 2 / 3)]
legit_test = legit_data[int(len(legit_data) * 2 / 3):]
NB_classifier.train(spam_train, legit_train)
spam_accuracy = 0
legit_accuracy = 0
for text in spam_test:
prediction = NB_classifier.predict(text)
spam_accuracy += prediction
spam_accuracy /= len(spam_test)
for text in legit_test:
prediction = NB_classifier.predict(text)
legit_accuracy += 1 - prediction
legit_accuracy /= len(legit_test)
print("Spam Text prediction accuracy:", spam_accuracy)
print("Legit Text prediction accuracy:", legit_accuracy)
text_spam = "Win Free Money now! Apply today and get cash!"
text_legit = "A usual day without anything interesting"
print(text_spam, NB_classifier.predict(text_spam))