Unwanted or unsolicited emails that are sent to users in large quantities are referred to as spam messages. The majority of messaging/emailing services automatically identify messages as spam in order to prevent them from filling users' inboxes needlessly. Typically, these communications are oddball and promotional in character. As a result, we could create ML/DL models that include spam message detection capabilities.
Identifying Spam Emails in Python with Tensorflow
To put it another way, we need to identify whether the letters are spam or ham. To that end, we will construct a TensorFlow-based spam detector in this post. This suggests that one example of a text classification issue is spam detection. Thus, we will be developing a text classification model and applying EDA to our dataset.
Importing Libraries
Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.
Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
Matplotlib/Seaborn/Wordcloud– This library is used to draw visualizations.
NLTK – Natural Language Tool Kit provides various functions to process the raw textual data.