The tf-idf value increases in proportion to the number of times a word appears in the document, but is often offset by the frequency of the word in the corpus, which helps to correct for the fact that some words appear the most often.
TF-IDF uses two statistical methods , the first of them — Term Frequency and the other — Inverse Document Frequency. Term frequency refers to the total number of times a given term t appears in a doc document, in relation to (per) the total number of all words in the document and the Inverse Document Frequency, which determines how much information a word provides. It measures the weight of a given word throughout the entire document. IDF shows how often or rarely a given word appears in all documents. 
TF-IDF can be calculated as tf * idf

Tf * Idf does not directly convert raw data into useful functions. First, it converts raw strings or dataset to vectors, and each word has its own vector. Then we will use a specific method to extract a function like Cosine Similarity that works with vectors, etc. As we know, we cannot directly pass a string to our model. So tf * idf gives us the numeric values ​​of the entire document. 
To extract elements from the word document, we import —

 from sklearn.feature_extraction.text import TfidfVectorizer 

Input:

 1st Sentence - "hello i am pulkit" 2nd Sentence - "your name is akshit" 

Code: Python code to find similarity measures

# importing libraries from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from sklearn.metrics import pairwise_distances from sklearn.metrics.pairwise import euclidean_distances from scipy.spatial import distance import pandas as pd import numpy as np   ## Convert 3D array to 1D array def arr_convert_1d (arr): arr = np.array (arr) arr = np.concaten ate (arr, axis = 0 ) arr = np.concatenate (arr, axis = 0 ) return arr   ## Cosine Similarity cos = [] def cosine (trans): cos.append (cosine_similarity (trans [ 0 ], trans [ 1 ]))    ## Manhattan Distance manhatten = [] def manhatten_distance (trans): manhatten.append (pairwise_distances (trans [ 0 ], trans [ 1 ],  metric = ` manhattan` ))   ## Euclidean distance euclidean = [] def euclidean_function (v ectors): euc = euclidean_distances (vectors [ 0 ], vectors [ 1 ]) euclidean.append (euc)    # This function finds similarities between the two # suggestions using the above functions.   ## TF - IDF def tfidf ( str1, str2): ques = [] # You must provide a dataset. Dataset link # is given at the end of this article. # and if you are using a different dataset, adjust   # according to columns and rows of your dataset   dataset = pd.read_csv ( `C : Users dell Desktop quora_duplicate_questions.tsv` ,  delimiter = `` , encoding = `utf-8` )     x = dataset.iloc [:, 1 : 5 ] x = x.dropna (how = `any` )      for k in range ( len (x)): for j in [ 2 , 3 ]:   ques.append (x.iloc [k, j]) vect = TfidfVectorizer () # Your entire dataset will fit. Ultimately this will # produce vectors based on words in the corpus / dataset vect.fit (ques)   corpus = [str1, str2]   trans = vect.transform (corpus)   euclidean_function (trans) cosine (trans)   manhatten_distance (trans)   return convert ()   def convert (): dataf = pd.DataFrame () lis2 = arr_convert_1d (manhatten) dataf [ `manhatten` ] = lis2 lis2 = arr_convert_1d (cos) dataf [ `cos_sim` ] = lis2 lis2 = arr_convert_1d (euclidean) dataf [ `euclidean` ] = lis2 return dataf    newData = pd.DataFrame ();  str1 = "hello i am pulkit " str2 = " your name is akshit " newData = tfidf (str1 , str2);  print (newData);

Output:

  manhatten cos_sim euclidean 0 2.955813 0.0 1.414214  

Dataset: Google Drive link
Note: The dataset is large, so it will take 30-40 seconds to display, and if you are going to work as it is, then it won`t work. This only works when you copy this code into your IDE and provide your dataset in the tfidf function.