diff --git a/text_classification/classify_text.py b/text_classification/classify_text.py
new file mode 100644
index 0000000..96b35b3
--- /dev/null
+++ b/text_classification/classify_text.py
@@ -0,0 +1,135 @@
+import nltk
+from nltk.stem.lancaster import LancasterStemmer
+import numpy as np
+import tflearn
+import tensorflow as tf
+import random
+import json
+import string
+import unicodedata
+import sys
+
+# a table structure to hold the different punctuation used
+tbl = dict.fromkeys(i for i in xrange(sys.maxunicode)
+                      if unicodedata.category(unichr(i)).startswith('P'))
+
+# method to remove punctuations from sentences.
+def remove_punctuation(text):
+    return text.translate(tbl)
+
+#initialize the stemmer
+stemmer = LancasterStemmer()
+#variable to hold the Json data read from the file
+data = None
+
+# read the json file and load the training data
+with open('data.json') as json_data:
+    data = json.load(json_data)
+    print (data)
+
+# get a list of all categories to train for
+categories = list(data.keys())
+words = []
+# a list of tuples with words in the sentence and category name 
+docs = []
+
+for each_category in data.keys():
+    for each_sentence in data[each_category]: 
+        # remove any punctuation from the sentence
+	each_sentence = remove_punctuation(each_sentence)
+	print each_sentence
+        # extract words from each sentence and append to the word list
+        w = nltk.word_tokenize(each_sentence)
+	print "tokenized words: ", w
+        words.extend(w)
+        docs.append((w, each_category))
+
+# stem and lower each word and remove duplicates
+words = [stemmer.stem(w.lower()) for w in words]
+words = sorted(list(set(words)))
+
+print (words)
+print (docs)
+
+# create our training data
+training = []
+output = []
+# create an empty array for our output
+output_empty = [0] * len(categories)
+
+
+for doc in docs:
+    # initialize our bag of words(bow) for each document in the list
+    bow = []
+    # list of tokenized words for the pattern
+    token_words = doc[0]
+    # stem each word
+    token_words = [stemmer.stem(word.lower()) for word in token_words]
+    # create our bag of words array
+    for w in words:
+        bow.append(1) if w in token_words else bow.append(0)
+    
+    output_row = list(output_empty)
+    output_row[categories.index(doc[1])] = 1
+    
+    # our training set will contain a the bag of words model and the output row that tells which catefory that bow belongs to.
+    training.append([bow, output_row])
+
+# shuffle our features and turn into np.array as tensorflow	 takes in numpy array
+random.shuffle(training)
+training = np.array(training)
+
+# trainX contains the Bag of words and train_y contains the label/ category
+train_x = list(training[:,0])
+train_y = list(training[:,1])
+
+# reset underlying graph data
+tf.reset_default_graph()
+# Build neural network
+net = tflearn.input_data(shape=[None, len(train_x[0])])
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, 8)
+net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
+net = tflearn.regression(net)
+
+# Define model and setup tensorboard
+model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
+# Start training (apply gradient descent algorithm)
+model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
+model.save('model.tflearn')
+
+
+# let's test the mdodel for a few sentences:
+# the first two sentences are used for training, and the last two sentences are not present in the training data. 
+sent_1 = "what time is it?"
+sent_2 = "I gotta go now"
+sent_3 = "do you know the time now?"
+sent_4 = "you must be a couple of years older then her!"
+
+# a method that takes in a sentence and list of all words 
+# and returns the data in a form the can be fed to tensorflow
+def get_tf_record(sentence):
+    global words
+    # tokenize the pattern
+    sentence_words = nltk.word_tokenize(sentence)
+    # stem each word
+    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
+    # bag of words
+    bow = [0]*len(words)  
+    for s in sentence_words:
+        for i,w in enumerate(words):
+            if w == s: 
+                bow[i] = 1
+
+    return(np.array(bow))
+
+
+# we can start to predict the results for each of the 4 sentences
+print( categories[np.argmax(model.predict([get_tf_record(sent_1)]))])
+print( categories[np.argmax(model.predict([get_tf_record(sent_2)]))])
+print( categories[np.argmax(model.predict([get_tf_record(sent_3)]))])
+print( categories[np.argmax(model.predict([get_tf_record(sent_4)]))])
+
+
+
+
diff --git a/text_classification/data.json b/text_classification/data.json
new file mode 100644
index 0000000..262a903
--- /dev/null
+++ b/text_classification/data.json
@@ -0,0 +1,7 @@
+{
+	"time": ["what time is it?", "how long has it been since we started?", "that's a long time ago", " I spoke to you last week", " I saw you yesterday"],
+	"sorry": ["I'm extremely sorry", "did he apologize to you?", "I shouldn't have been rude"],
+	"greeting": ["Hello there!", "Hey man! How are you?", "hi"],
+	"farewell": ["It was a pleasure meeting you", "Good Bye.", "see you soon", "I gotta go now."],
+	"age": ["what's your age?", "How old are you?", "I'm a couple of years older than her", "You look aged!"]
+}