Spam Classification Project: Identifying Email Threats



To make it easier for you to go through the project, it would be nice to know the following topics:

P.S. Even without knowledge of these topics, you can complete the project.

The detection of spam emails, also known as email filtering, is identifying and separating unwanted or unsolicited email messages from legitimate ones. This is typically done using a combination of techniques such as machine learning, natural language processing, and rule-based filtering.

One common method of spam detection is the use of Bayesian filters. Bayesian filters use statistical analysis to determine the likelihood that an email is a spam based on the words and phrases it contains. The filter is trained on a set of known spam and non-spam emails and uses this information to classify new emails as spam or not.

Another method is the use of machine learning algorithms such as Random Forest, Neural Networks, and Support Vector Machines (SVMs) to classify emails as spam or not. These algorithms are trained on a set of labeled email data, and then they use this training to classify new emails based on their content, sender, and other characteristics.

Rule-based filtering is another technique used to detect spam emails. This method uses a set of predetermined rules, such as keywords or regular expressions, to identify and flag potential spam emails.

Another method is using the characteristics of the sender's address, for example, if the email is coming from a known spammer address or if the email has a high number of recipients, it is likely to be spam.

It is worth noting that spam detection is a constantly evolving field as spammers are always finding new ways to bypass filters, so these techniques are constantly being updated and improved to stay ahead of spammers. Let's get started!

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