Datasets:

rr

/

dd

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Classification

from nltk.corpus import names

l = ([(name, 'male') for name in names.words('male.txt')] +

[(name, 'female') for name in names.words('female.txt')])

print("\nNumber of male names:")

print(len(names.words('male.txt')))

print("\nNumber of female names:")

print(len(names.words('female.txt')))

male_names = names.words('male.txt')

female_names = names.words('female.txt')

print("\nFirst 10 male names:")

print(male_names[0:15])

print("\nFirst 10 female names:")

print(female_names[0:15])

import random

random.shuffle(n)

def gender_features(word):

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

feature_sets = [(gender_features(n), gender) for (n, gender) in l]

train_set, test_set = feature_sets[1000:], feature_sets[:1000]

from nltk import NaiveBayesClassifier

model = NaiveBayesClassifier.train(train_set)

model.classify(gender_features('#whatever he asks'))

model.classify(gender_features('#whatever he asks'))

Clustering

Hierarchical

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.cluster import KMeans

documents = ['Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much.',

'They were the last people you’d expect to be involved in anything strange or mysterious, because they just didn’t hold with such nonsense.',

'Mr. Dursley was the director of a firm called Grunnings, which made drills.',

'He was a big, beefy man with hardly any neck, although he did have a very large mustache.',

'Mrs. Dursley was thin and blonde and had nearly twice the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbors.',

'The Dursley s had a small son called Dudley and in their opinion there was no finer boy anywhere.']

documents

vectorizer = TfidfVectorizer(stop_words = 'english')

X = vectorizer.fit_transform(documents)

terms = vectorizer.get_feature_names()

from sklearn.metrics.pairwise import cosine_similarity

dist = 1- cosine_similarity(X)

dist

import matplotlib.pyplot as plt

from scipy.cluster.hierarchy import ward, dendrogram

linkage_matrix = ward(dist)

fig, ax = plt.subplots(figsize = (8,8)) #set size

ax = dendrogram(linkage_matrix, orientation = 'right', labels = documents);

plt.tick_params(\

axis = 'x',

which = 'both',

bottom = 'off',

top = 'off',

labelbottom = 'off')

plt.tight_layout()

K Means

model = KMeans(n_clusters = 2, init = 'k-means++', max_iter = 100, n_init = 1)

model.fit(X)

# top ten terms/words per cluster

order_centroids = model.cluster_centers_.argsort()[:, ::-1]

terms = vectorizer.get_feature_names()

for i in range(2):

print("Cluster Number:", i),

for c in order_centroids[i, :10]:

print('%s' % terms[c])

Y = vectorizer.transform(["Harry Potter is not Harry Styles"])

model.predict(Y)

Preprocessing

External Data Preprocessing(importing dataset, defining the function)

import re

import nltk

import inflect

from nltk import word_tokenize, sent_tokenize

from nltk.corpus import stopwords

from nltk.stem import LancasterStemmer, WordNetLemmatizer

file = open("dataset path.txt", encoding = 'utf-8').read()

words = word_tokenize(file)

def to_lowercase(words):

#'''Convert all the characters into lowercase from the list of tokenized words'''

new_words = []

for word in words:

new_word = word.lower()

new_words.append(new_word)

return new_words

words = to_lowercase(words)

#print(words)

def remove_punctuation(words):

#'''Remove all the punctuation marks from the list of tokenized words'''

new_words = []

for word in words:

new_word = re.sub(r'[^\w\s]', '', word)

if new_word != '':

new_words.append(new_word)

return new_words