diff --git a/backend/.gitignore b/backend/.gitignore
index e69de29b..578f873a 100644
--- a/backend/.gitignore
+++ b/backend/.gitignore
@@ -0,0 +1 @@
+word2vec11.model/
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diff --git a/backend/info.md b/backend/info.md
new file mode 100644
index 00000000..ffafb5f2
--- /dev/null
+++ b/backend/info.md
@@ -0,0 +1,7 @@
+python version== 3.9.5
+gensim version== 3.6.0
+nltk version==3.6.7
+sklearn version 1.0.2
+pandas version== 1.4.0
+matplotlib version== 3.5.1
+re version== 2.2.1
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diff --git a/backend/task.py b/backend/task.py
new file mode 100644
index 00000000..35333419
--- /dev/null
+++ b/backend/task.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+from sklearn.cluster import KMeans
+import pandas as pd
+import numpy as np
+import re
+import nltk
+import os
+
+from sqlalchemy import column
+
+from gensim.models import Word2Vec
+from time import time  # To time our operations
+from collections import defaultdict  # For word frequency
+import logging  # Setting up the loggings to monitor gensim
+logging.basicConfig(format="%(levelname)s - %(asctime)s: %(message)s", datefmt= '%H:%M:%S', level=logging.INFO)
+
+import matplotlib.pyplot as plt
+
+
+# nltk.download()
+path = input("Enter the file Path")
+df = pd.read_json (path)
+df.to_csv (r'floop_data_15k.csv', index = None)
+
+data = pd.read_csv("floop_data_15k.csv")
+
+data.columns= ["Field1"]
+
+data.shape
+
+
+#To remove special characters and punctuation from our dataset
+from string import punctuation
+
+punctuations = punctuation
+
+def solution(sentence):
+    for p in punctuations:
+        sentence = sentence.replace(p, '')
+    return sentence
+
+x = data["Field1"].apply(solution)
+pattern = "[^a-zA-Z0-9]"
+x_cleaned = [re.sub(pattern," ",text) for text in x]
+
+x_lowered = [text.lower() for text in x_cleaned]
+x_lowered
+
+x_lowered[0]
+
+nltk.download('punkt')
+nltk.download('omw-1.4')
+
+x_tokenized = [nltk.word_tokenize(text) for text in x_lowered]
+
+nltk.download('wordnet')
+from nltk.stem import WordNetLemmatizer
+lemma = WordNetLemmatizer()
+
+x_lemmatized = [[lemma.lemmatize(word) for word in text] for text in x_tokenized]
+
+print(x_lemmatized[0])
+
+
+
+
+
+# For classification data whether good or bad.
+
+w2v_model = Word2Vec(min_count=20,window=2,sample=6e-5, alpha=0.03, min_alpha=0.0007, negative=20,workers= 1 )
+
+t = time()
+
+w2v_model.build_vocab(x_lemmatized, progress_per=10000)
+
+print('Time to build vocab: {} mins'.format(round((time() - t) / 60, 2)))
+
+t = time()
+
+w2v_model.train(x_lemmatized, total_examples=w2v_model.corpus_count, epochs=30, report_delay=1)
+
+print('Time to train the model: {} mins'.format(round((time() - t) / 60, 2)))
+
+w2v_model.init_sims(replace=True)
+
+w2v_model.save("word2vec11.model")
+
+word_vectors = Word2Vec.load("word2vec11.model").wv
+model = KMeans(n_clusters=2, max_iter=1000, random_state=True, n_init=50).fit(X=word_vectors.vectors.astype('double'))
+positive_cluster_center = model.cluster_centers_[0]
+negative_cluster_center = model.cluster_centers_[1]
+
+model.cluster_centers_
+
+def cast_vector(row):
+    return np.array(list(map(lambda x: x.astype('double'), row)))
+
+words = pd.DataFrame(word_vectors.vocab.keys())
+words.columns = ['words']
+words['vectors'] = words.words.apply(lambda x: word_vectors[f'{x}'])
+words['vectorsmean'] = words.vectors.apply(lambda x: x.mean())
+words['vectors_typed'] = words.vectors.apply(cast_vector)
+words['cluster'] = words.vectors_typed.apply(lambda x: model.predict([np.array(x, dtype=np.double)]))
+words.cluster = words.cluster.apply(lambda x: x[0])
+words['cluster_value'] = [1 if i==0 else -1 for i in words.cluster]
+words['closeness_score'] = words.apply(lambda x: 1/(model.transform([x.vectors]).min()), axis=1)
+words['sentiment_coeff'] = words.closeness_score * words.cluster_value
+
+words.head(10)
+
+u_labels = np.unique(words['cluster'])
+
+words['vectorsmean'] = words.vectors.apply(lambda x: x.mean())
+
+words['vectorsmean'][0]
+
+len(words["vectors"][1])
+
+words.head(10)
+
+# FOr plotting 
+
+colors = {1: 'black', -1: 'Red'}
+plt.scatter(words['sentiment_coeff'] , words['vectorsmean'] , c=words['cluster_value'].map(colors))
+
+plt.show()
+
+os.remove("floop_data_15k.csv")
+os.remove("word2vec11.model")
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