The detection of spam emails, also known as **email filtering**, involves 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 spam based on the words and phrases it contains. The filter learns from a set of known spam and non-spam emails and uses this information to **classify new emails** as spam or not.

Another method involves 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 **dataset of labeled emails**, and they use this training to classify new emails based on their content, sender, and other characteristics.

**Rule-based filtering** is another technique employed in spam detection. This method utilizes a set of predetermined rules, such as keywords or regular expressions, to identify and flag potential spam emails.

Additionally, analyzing the **characteristics of the sender's address** can be effective. For example, emails coming from a known spammer's address or those sent to a high number of recipients are likely to be spam.

It is worth noting that **spam detection** is a constantly evolving field. As spammers continually devise new methods to bypass filters, the techniques used for spam detection must also be frequently updated and refined to stay ahead of these threats.

In this project, we are going to classify spam emails according to their content.

In this project, we are going to classify spam email according to their content.