SPAM Classification System

Overview

A classic NLP project to identify spam messages. This system uses a Bag of Words model combined with several classification algorithms to accurately filter out spam.

Approach

1. Text Preprocessing: Tokenization, stop-word removal, and stemming.
2. Feature Extraction: CountVectorizer (Bag of Words).
3. Modeling: Trained Random Forest, Decision Tree, and Naive Bayes models.

Results

• Naive Bayes showed the best performance for this specific text classification task, balancing precision and recall effectively.