diff --git a/nlp.py b/nlp.py
new file mode 100644
index 0000000..036e929
--- /dev/null
+++ b/nlp.py
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+
+
+import json
+import pandas as pd
+import nltk
+import numpy as np
+from nltk.stem.porter import *
+from nltk.stem import WordNetLemmatizer, SnowballStemmer
+from gensim.parsing.preprocessing import STOPWORDS
+from gensim.utils import simple_preprocess
+import gensim
+
+
+from sklearn.datasets import fetch_20newsgroups
+newsgroups_train = fetch_20newsgroups(subset='train', shuffle=True)
+newsgroups_test = fetch_20newsgroups(subset='test', shuffle=True)
+
+np.random.seed(400)
+
+
+stemmer = SnowballStemmer("english")
+
+# context = ssl._create_unverified_context()
+
+
+def get_data():
+    pass
+
+
+def lemmatize_stemming(text):
+    # Tokenize and lemmatize
+    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))
+
+
+def preprocess(text):
+    result = []
+    for token in gensim.utils.simple_preprocess(text):
+        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:
+            result.append(lemmatize_stemming(token))
+
+    return result
+
+
+def categorize_str(s: str) -> int:
+    """
+    Takes in a string to determine which topic it belongs to
+    Returns the topic number as an int
+    """
+    bow_vector = dictionary.doc2bow(preprocess(s))
+    ldaResults = sorted(lda_model[bow_vector], key=lambda tup: -1*tup[1])
+    return ldaResults[0][0]
+
+
+def create_model(documents: list):
+    """
+    Takes a list of strings to create model
+    returns the lda model and dictionary
+    """
+    processed_docs = []
+    for doc in newsgroups_train.data:
+        processed_docs.append(preprocess(doc))
+    dictionary = gensim.corpora.Dictionary(processed_docs)
+    bow_corpus = [dictionary.doc2bow(doc) for doc in processed_docs]
+    lda_model = gensim.models.LdaMulticore(bow_corpus,
+                                           num_topics=7,
+                                           id2word=dictionary,
+                                           passes=10,
+                                           workers=2)
+    return(lda_model, dictionary)
+
+
+lda_model, dictionary = create_model(newsgroups_train.data)
+for idx, topic in lda_model.show_topics(formatted=False, num_words=30):
+    print('Topic: {} \nWords: {}'.format(idx, [w[0] for w in topic]))
+
+for ind in range(len(newsgroups_test)):
+    unseenDoc = newsgroups_test.data[ind]
+    print(ind, categorize_str(unseenDoc))