SMS Spam Detection Using LSTM – A Hands On Guide!
Introduction
In today’s world, almost everyone is using a mobile phone and all of them will receive messages(SMS/ email) daily on their phone. But the main thing is that many of the received messages will be spam and only a few of them are ham or required messages.
In this article, we are going to create an SMS spam detection model which will help you to find whether an SMS is spam or not using LSTM.
About Dataset: Here we are using SMS Spam Detection Dataset which contains SMS text and its corresponding label( Spam or Ham).
Implementation
First of all, we are importing all the required libraries for data preprocessing
import pandas as pd
import numpy as np
import re
import collections
import contractions
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
import warnings
warnings.simplefilter(action='ignore', category=Warning)
import keras
from keras.layers import Dense, Embedding, LSTM, Dropout
from keras.models