Spam detection is a common application of machine learning in email systems. It involves classifying emails as spam or not spam.
This is a classification problem, where the model learns from labeled data. The dataset contains emails marked as spam or legitimate.
Features used in spam detection include:
Email content
Sender information
Frequency of certain words
The model is trained using algorithms such as Naive Bayes, Logistic Regression, or Decision Trees.
Once trained, the model predicts whether a new email is spam. If classified as spam, the email is filtered out or moved to a spam folder.
Evaluation metrics such as precision and recall are important in spam detection. High precision ensures that legitimate emails are not marked as spam.
Challenges include evolving spam techniques and maintaining accuracy over time.
Spam detection systems improve user experience and protect users from unwanted or harmful content.
