Answer / Vivek Kumar Pranami

Designing an email spam filter typically involves a combination of several techniques:
1. Text analysis: Extract features from the email content and subject line, such as frequency of certain words or phrases associated with spam.
2. Header analysis: Check for headers that are commonly found in spam emails (e.g., 'Re:' multiple times).
3. Sender analysis: Evaluate the sender's reputation, checking blacklists and known spam domains.
4. Machine learning: Train a classifier on a labeled dataset of spam and non-spam emails using various machine learning algorithms (e.g., Naive Bayes, Support Vector Machines).

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