Spam or Ham

Your inbox is under attack. Every day, hundreds of suspicious messages slip past the old rule-based filter — Nigerian princes, miracle pills, and "urgent" account alerts. The security team has handed you a labeled dataset of 1,000 messages and one mission: build a machine-learning classifier that can tell spam from ham. Vectorize the text, train a model, and prove it works.