def extract_features(word, N=2):
last_n_letters = word[-N:]
return {'feature': last_n_letters.lower()}
if __name__ == '__main__':
male_list = [(name, 'male') for name in names.words('male.txt')]
female_list = [(name, 'female') for name in names.words('female.txt')]
data = (male_list + female_list)
random.seed(5)
random.shuffle(data)
namesInput = ['Rajesh', 'Gaurav', 'Swati', 'Shubha']
train_sample = int(0.8 * len(data))
for i in range(1, 6):
print('\nNumber of end letters:', i)
features = [(extract_features(n, i), gender) for (n, gender) in data]
train_data, test_data = features[:train_sample], features[train_sample:]
classifier = NaiveBayesClassifier.train(train_data)
accuracy_classifier = round(100 * nltk_accuracy(classifier, test_data), 2)
print('Accuracy = ' + str(accuracy_classifier) + '%')
for name in namesInput:
print(name, '==>', classifier.classify(extract_features(name, i)))
