In an era of flooded emails in our inboxes, accurate and efficient spam filtering is essential for managing email communication. 💌 But have you ever wondered how those spam emails are detected and filtered out? Join me on a journey as we dive into the world of spam filtering using three different Naive Bayes algorithms!
In my recent project, I worked on spam filtering by implementing and comparing three Naive Bayes algorithms: GaussianNB, BernoulliNB, and MultinomialNB. Despite their "naive" assumptions, these algorithms offer impressive results in identifying and categorizing spam emails.
📝 For text preprocessing, NLTK (Natural Language Toolkit) serves as a powerful tool for, offering functionalities such as tokenization, stemming, and stopword removal, essential to clean and structure raw text data for effective analysis and NLP tasks.
In this project, I not only built a robust spam filtering system but also gained a deeper understanding of the inner workings of Naive Bayes algorithms. The knowledge gained can be extended to other classification tasks and forms a strong foundation for further exploration into the world of natural language processing (NLP).