🔗 GitHub
Description
This project aims to develop a spam classification system that can accurately classify messages as spam or not spam using natural language processing techniques. The project will use NLTK and TfIdf Vectorizer to preprocess and represent the text data, and the Naive Bayes algorithm to build a classification model.
In addition to the classification model, the project will also include data visualization using Seaborn to provide insights into the dataset and the performance of the model. The visualization also involves wordclouds which will help to identify patterns and trends in the data, as well as evaluate the accuracy of the classification model.