SMS Spam detection model using LSTM

SMS Spam detection model using LSTM

DataSet Source: https://www.kaggle.com/uciml/sms-spam-collection-dataset

https://github.com/kvsivasankar/RNN-Models/blob/master/SMSSpam_BinaryTextClassification_Words_LSTM.ipynb

SMS spam detection model (LSTM)

DataSet Source: https://www.kaggle.com/uciml/sms-spam-collection-dataset

Machine learning model to classify sms texts as ham or spam

·      Total 11144 data points (rows) presented In the dataset

·      Splited data into train and test  (80% and 20%) ratio.

·      Used torchtext library for text processing.

·      Used NLTK library to word tokenization.

·      Built vocabulary using top 10,500 words from the testing data

In this model I used LSTM architecture for to predict. LSTM is improved version of RNN

The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.

NLTK provides a function called word_tokenize() for splitting strings into tokens. It splits tokens based on white space and punctuation.

 

Next, we have to build a vocabulary. This is a effectively a look up table where every unique word in your data set has a corresponding index (an integer). Each index is used to construct a one-hot vector for each word.

Got training accuracy 86% but test accuracy 74%.