Chor diya 💜

 Usne dil ka haal batana chhor diya

Hamne bhi gehrayi mein jana chhor diya

Jab usko he doori ehsas nahi

Hamne bhi ehsas dilana chhor diya

Maine kaha raaste hai dushwar bahut

Usne tab se sath nibhana chhor diya 💔

Kaun hun mai....❤️

 Koi tumse puche kaun hun main

Tum kah dena koi khas nahin

Ek dost hai kaccha pakka sa

Ek jhooth hai aadha saccha sa

Ek khwab adhura pura sa

Ek phool hai rukha sukha sa

Ek sapna hai bin socha sa

Ek apna hai andekha sa

Ek rishta hai anjana sa

Hakikat mein afsana sa   

                       

 Kuchh pagal sa deewana sa

Bus ek bahana achcha sa

Jivan ka aisa sathi hai

Jo door ho to kuchh pass nahin

Koi tumse puche kaun hun main

Tum kah dena koi khas nahin

DEAR LOVE❣️

 1. Kho kar phir tum mujhe paa na sakhoge,

 Hum waha milenge jaha tum aa na sakhoge❤️”

2 . Aankhein padho aur jaano humari raza kya hai,

Har baat agar lafzon se ho toh maza kya hai?”

𝐂𝐇𝐈𝐍𝐓𝐔❤️

Kash tu chaand aur mai sitarah hota

Aasmaan mein aashiyana hamara hota,

Log tumhe door se dekhte,

Pas se dekhne ka haq sirf hamara hota❣️❣️❣️❣️

import nltk,re

import Levenshtein

from nltk.metrics.distance import edit_distance

import string

import pandas as pd

import re, nltk,string

import nltk

nltk.download('punkt')

nltk.download('stopwords')

nltk.download('maxent_ne_chunker')

nltk.download('words')

nltk.download('averaged_perceptron_tagger')

nltk.download('stopwords')

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize,sent_tokenize

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize, sent_tokenize

from nltk.probability import FreqDist

import matplotlib.pyplot as plt

from collections import Counter

# Download necessary NLTK data

nltk.download('punkt')

nltk.download('stopwords')

from nltk import word_tokenize,sent_tokenize ,ne_chunk,pos_tag

paragraph = "i. It is a nice night.ii. This crap game is over a garage in Fifty-second Street... 3iii. …Nobody ever takes the newspapers she sells ...iv. …I am sitting in Mindy’s restaurant putting on the gefillte fish, which is a dish I am very fond of, ...v. The quick brown fox jumps over the lazy dog."

paragraph=paragraph.lower()

paragraph = nltk.sent_tokenize(paragraph)

# print(paragraph)

okk=[]

for i in paragraph:

    # print(i)

    ok=nltk.word_tokenize(i)

    for jj in ok:

        okk.append(jj)

from spellchecker import SpellChecker

def find_misspelled_words(words):

    spell = SpellChecker()

    misspelled = spell.unknown(words)

    

    return misspelled

misspelled_words = find_misspelled_words(okk)

print(misspelled_words)

import Levenshtein as lev

from spellchecker import SpellChecker

spell = SpellChecker()

# def suggest_corrections(misspelled_words):

#     suggestions = {}

#     for word in misspelled_words:

#         # Get close matches

#         candidates = spell.candidates(word)

#         # Suggest based on Levenshtein distance

#         suggestions[word] = sorted(candidates, key=lambda candidate: lev.distance(word, candidate))

#     return suggestions

# corrections = suggest_corrections(misspelled_words)

# # print(misspelled_count)

# print(corrections)

#before cleaning 

print(len(okk))

from nltk.stem.porter import PorterStemmer

stemming_text = []

for i in okk:

    stemming_text.append(PorterStemmer().stem(i))

    

print(stemming_text)

word_freq = Counter(okk)

print(word_freq)

# Get the 15 most common words

most_common_words = word_freq.most_common(15)

words = []

counts = []

for word, count in most_common_words:

    words.append(word)

    counts.append(count)

# print(counts)

# Plot frequency distribution

plt.figure(figsize=(10,6))

plt.bar(words, counts)

plt.title('Top 15 Most Common Words')

plt.xlabel('Words')

plt.ylabel('Frequency')

plt.xticks(rotation=45)

plt.show()

from wordcloud import WordCloud

word_string = ' '.join(okk)

# Generate word cloud

wordcloud = WordCloud(width=800, height=400, background_color='white').generate(word_string)

# Display the word cloud

plt.figure(figsize=(10, 6))

plt.imshow(wordcloud, interpolation='bilinear')

plt.axis('off')

plt.show()

mostcommonword = Counter(okk)

print(mostcommonword)

uniqueword = set(okk)

print(len(uniqueword))

##################################################################### cleaning start

# paragraph=paragraph.translate(paragraph.maketrans("","",string.punctuation))

# print(paragraph)

# print()

# sentences = sent_tokenize(paragraph)

# from nltk.corpus import stopwords

# filterword =[]

# stp = nltk.corpus.stopwords.words("english")

# for kk in sentences:

#     print(kk)

#     tokk = nltk.word_tokenize(kk)

#     print(tokk)

#     print()

    

#     print()

#     for i in tokk:

#         if i.isalpha():

#             if i  not in stp:

#                 filterword.append(i)

        

# # print(sentences)

# print(filterword)

# tagg = []

# postt =nltk.pos_tag(filterword)

# tagg.append(postt)

# print(tagg)

# print()

# print()

# ner_result = ne_chunk(postt)

# print(ner_result)

# print()

# print()

# print()

# print()

# print()

# print()

# print()

# # now i will count the words

# paragraph = nltk.sent_tokenize(paragraph)

s1 = "blokchn"

s2 = "blockchain"

edit_distanc = Levenshtein.distance(s1,s2)

print("the edit distance between'{}' and this '{}' is '{}'".format(s1,s2,edit_distanc))

Mysteries of the Heart🖤

 Jaane kya mujhse zamana chahta hai,

mera dil tod kar mujhe hasana chahta hai, Jaane kya baat jhalakti he mere chehre se, har saks mujhe aazmana chahta hai?”