#! python3

def gender_features(word):

return { 'last_letter' : word[-1] }

#end

import random

import nltk

names = ([(name,'male') for name in ('D:\Project_files_notes\Gender-Identifier\male.txt')] + [(name,'female') for name in ('D:\Project_files_notes\Gender-Identifier\female.txt')])

random.shuffle(names)

featuresets = [(gender_features(n),g) for (n,g) in names]

from nltk.classify import apply_features

train_set = apply_features(gender_features,names[86:])

test_set = apply_features(gender_features, names[:85])

classifier = nltk.NaiveBayesClassifier.train(train_set)

print classifier.classify(gender_features('ravi'))

print nltk.classify.accuracy(classifier, test_set)

classifier.show_most_informative_features(5)