Upload tara ja jeet.txt

tara ja jeet.txt

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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
+
+words = remove_punctuation(words)
+#print(words)
+def replace_numbers(words):
+    #'''Replace all integer occurrences in the list of tokenized words'''
+    p = inflect.engine()
+    new_words = []
+    for word in words:
+        if word.isdigit():
+            new_word = p.number_to_words(word)
+            new_words.append(new_word)
+        else:
+            new_words.append(word)
+    return(new_words)
+
+words = replace_numbers(words)
+#print(words) 
+def remove_stopwords(words):
+    #'''Remove stop words from the list of tokenized words'''
+    new_words = []
+    for word in words:
+        if word not in stopwords.words('english'):
+            new_words.append(word)
+    return new_words
+
+words = remove_stopwords(words)
+#print(words)
+def stem_words(words):
+    #'''Finding stem words in the list of tokenized words'''
+    stemmer = LancasterStemmer()
+    stems = []
+    for word in words:
+        stem = stemmer.stem(word)
+        stems.append(stem)
+    return stems
+
+words = stem_words(words)
+#print(words)
+
+def lemmatize_words(words):
+    #'''Lemmatize verbs in the list of tokenized words'''
+    lemmatizer = WordNetLemmatizer()
+    lemmas = []
+    for word in words:
+        lemma = lemmatizer.lemmatize(word, pos = 'v')
+        lemmas.append(lemma)
+    return lemmas
+
+words = lemmatize_words(words)
+#print(words)
+print(words)
+Text preprocessing(non user defined)
+import nltk
+import re
+import string
+import inflect
+from nltk.corpus import stopwords
+from nltk import word_tokenize
+series = open("dataset path.txt".txt").read()
+series
+series_lower = series.lower()
+# Removal of numbers
+result1 = re.sub(r'\d+', '', series_lower)
+#result1
+# Removal of punctuations
+result2 = result1.translate(str.maketrans('','',string.punctuation))
+#result2
+# Removing white spaces
+result3 = result2.strip()
+#result3
+# Removal of stopwords
+# Tokenize the text
+result3_tokens = word_tokenize(result3)
+#result3_tokens
+# Removing stopwords
+sw = set(stopwords.words('english'))
+
+result4 = []
+for w in result3_tokens:
+    if w not in sw:
+        result4.append(w)
+#result4
+text_tokenize = result4
+#text_tokenize
+output = nltk.pos_tag(text_tokenize)
+#output
+Sentiment Analysis
+import pandas as pd
+import re
+import string
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+from nltk.stem import WordNetLemmatizer
+import nltk
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+file = open("dataset path.txt".txt", encoding = 'utf-8').read()
+# These are not required. DO Only if asked.
+# this code, clean data 2 and clean data 3
+cleandata1 = file.lower()
+#cleandata1
+cleandata2 = re.sub(r'[^\w\s]','', cleandata1)
+#cleandata2
+cleandata3 = re.sub(r'\d+', ' ', cleandata2)
+#cleandata3
+stop_words = set(stopwords.words('english'))
+#stop_words
+#let us remove them using function removeWords()
+tokens = word_tokenize(cleandata3)
+cleandata4 = [i for i in tokens if not i in stop_words]
+cleandata4
+cleandata4 = " ".join(str(x) for x in cleandata4)
+#cleandata4
+cleandata5 = ' '.join(i for i in cleandata4.split() if not (i.isalpha() and len(i)==1))
+#cleandata5
+cleandata6 = cleandata5.strip()
+#cleandata6
+## Frequency of words
+words_dict = {}
+for word in cleandata6.split():
+    words_dict[word] = words_dict.get(word, 0)+1
+for key in sorted(words_dict):
+    print("{}:{}".format(key,words_dict[key]))
+wordcloud = WordCloud(width=480, height=480, margin=0).generate(cleandata6)
+
+# Display the generated image:
+plt.imshow(wordcloud, interpolation='bilinear')
+plt.axis("off")
+plt.margins(x=0, y=0)
+plt.show()
+#with max words
+wordcloud = WordCloud(width=480, height=480, max_words=5).generate(cleandata6)
+plt.figure()
+plt.imshow(wordcloud, interpolation="bilinear")
+plt.axis("off")
+plt.margins(x=0, y=0)
+plt.show()
+from textblob import TextBlob
+from textblob.sentiments import NaiveBayesAnalyzer
+
+Bag of Words
+from sklearn.feature_extraction.text import CountVectorizer 	
+sentences = ["Hello how are you",
+             "Hi students are you all good",
+            "Okay lets study bag of words"]
+sentences
+cv = CountVectorizer()
+bow = cv.fit_transform(sentences).toarray()
+cv.vocabulary_
+cv.get_feature_names()
+bow
+
+NLTK Basics
+
+import nltk
+from nltk.book import *
+#similar 
+text6.similar('King')
+text6.concordance('King')
+sents()
+len(text1)
+#lines tells how many lines you want. You can run the code without the lines also
+text3.concordance('lived', lines = 38)
+text3.common_contexts(['earth', 'heaven'])
+text1.common_contexts(['captain', 'whale'])
+#text3.collocations()
+text3.collocation_list()
+#Put number inside bracket to get only how many is required
+text6.collocation_list(5)
+text6.generate(5)
+len(text3)
+from nltk import lm
+help(lm)
+text = "Hello students, we are studying Parts of Speech Tagging. Lets understand the process of\
+            shallow parsing or Chunking. Here were are drawing the tree corresponding to the words \
+            and the POS tags based on a set grammer regex patter."
+words = nltk.word_tokenize(text)
+#words
+tags = nltk.pos_tag(words)
+#tags
+# idk what this is
+grammar = (''' NP: {<DT><JJ><NN>} ''')
+grammar
+freq = FreqDist(text3)
+freq
+freq.most_common(50)
+freq['father']
+freq.plot(20, cumulative = True)
+freq.plot(20)
+freq.tabulate()
+freq.max()	
+[i for i in sent3 if len(i) > 8]
+[i for i in sent3 if len(i) != 3]
+[i for i in sent3 if len(i) <= 3]
+l = []
+for i in sent3:
+    if((len(i)) <= 3):
+        l.append(i)
+print(l)
+# print(len(l))
+
+Simple Regex
+Regex on strings¶
+
+import re
+
+egstring = '''
+Jessica is 15 years old, and Daniel is 27 years old.
+Edward is 97 years old, and his grandfather, Oscar, is 108 years old
+'''
+
+ages = re.findall(r'\d{1,3}', egstring)
+names = re.findall(r'[A-Z][a-z]*', egstring)
+
+print(ages)
+print(names)
+
+result = re.split(r'\d{1,3}', egstring)
+print(result)
+
+string = "Python is fun"
+
+match = re.search('\APython', string)
+
+if match:
+    print("pattern found inside the string")
+else:
+    print("pattern not found")
+
+Email
+
+# example
+#pattern = r'\w{4}_\d{2}\w{5}.\w{4}@w{5}.\w{3}
+#pattern1 = r'[a-z]+_[0-9a-z]+.[a-z]+@[a-z.]+'
+#email_string = "bill_05gates.mics@gmail.com"
+
+generic_pattern = r'[a-zA-Z0-9._]+@[a-z]+.[a-z]+'
+email_string1 = "lalitisdashing_6969@gmail.com"
+
+if(re.match(generic_pattern, email_string1) != None):
+    print(True)
+else:
+    print(False)
+
+# Entering an Email
+email = input ("Enter an email")
+email_list = ["bill_05gates.mics@gmail.com", "sahithi12_kanithi12@hotmail.com", " xyz@gmail.com",
+             "sachin.tripathi007@hotmail.com", "tripathi.sachin13@gmail.com", "qtdash@yahoo.com"]
+email_list.append(email)
+print(email_list)
+
+#Function definition
+def email_match(email_ls):
+    count = len(email_ls)
+    gmail_pattern = r'[a-zA-Z0-9._]+@gmail.[a-z]+'
+    hotmail_pattern = r'[a-zA-Z0-9._]+@hotmail.[a-z]+'
+    yahoo_pattern = r'[a-zA-Z0-9._]+@yahoo.[a-z]+'
+    print("---")
+    
+    print("GMAIL MAILS")
+    for i in range(0,count):
+        if(re.match(gmail_pattern, email_ls[i]) != None):
+            print(email_ls[i])
+    print("---")
+    
+    print("HOTMAIL MAILS")
+    for i in range(0,count):
+        if(re.match(hotmail_pattern, email_ls[i]) != None):
+            print(email_ls[i])
+    print("---")
+    
+    print("YAHOO MAILS")
+    for i in range(0,count):
+        if(re.match(yahoo_pattern, email_ls[i]) !=None):
+            print(email_ls[i])
+    
+#Calling the function
+email_match(email_list)
+
+POS
+
+import nltk
+from nltk import pos_tag
+from nltk import word_tokenize
+sample_text = word_tokenize("The classes are reopening on 15th March in St. Joseph's College of Commerce")
+sample_text
+
+pos_tag(sample_text)
+
+nltk.help.upenn_tagset("DT")
+
+nltk.help.upenn_tagset("VBP")
+
+# do for what is asked or how many ever are asked
+#nltk.help.upenn_tagset("NNS")
+
+text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())
+text
+
+text.similar("boy")
+
+text.similar("test")
+
+var1 = nltk.tag.str2tuple("SJCC/NNP")
+
+var1
+
+var1[1]
+
+sentence = '''
+           The/DT classes/NNS are/VBP reopening/VBG from/IN 15th/CD March'2021/NNP in/IN St./NNP Joseph/NNP 's/POS College/NNP
+           '''
+sentence
+
+abc = [nltk.tag.str2tuple(i) for i in sentence.split()]
+abc
+
+abc = [nltk.tag.str2tuple(i) for i in sentence.split()]
+abc
+
+nltk.corpus.brown.tagged_words()
+
+nltk.help.brown_tagset('AT')
+
+nltk.help.brown_tagset('NP-TL')
+
+nltk.corpus.indian.tagged_words()
+
+nltk.help.indian_tagset('SYM')