Spam Email Detection

Open-ended design + light coding: from a large email corpus, pick spam signals, propose a classification approach, and define accuracy metrics (precision, recall, F1, false-positive rate), reasoning explicitly about the catch-more-spam vs block-real-mail trade-off. The Challenge You have a large collection of past emails. Your task is to design and build a system that identifies spam. This problem does not have one specific answer. You need to figure out what signs (signals) show that an email is spam. You must explain how you will sort emails based on these signs. Finally, you need to explain how you will measure if your system is accurate. Interview Goals This question tests your problem solving skills. You are expected to: Ask questions to make the problem clear. Suggest rules for classification. Discuss standard metrics like Precision, Recall, F1 Score, and False Positive Rate. Write the class-level code for the system. The interviewer wants to see how you break down a vague problem. They care about how you handle trade-offs, such as catching more spam versus accidentally blocking real emails. They also want to see if you can turn your ideas into actual code.