We will use a package called eagetmail to load and extract information from the EML file.
N What are the solutions to this problem ?We will build a classification model to mark potential spam according to the subject line of the message.
We will rely on the ML model to learn how to classify these suspicious messages statistically. N Why is this a problem ?It’s difficult to strike the right balance between filtering suspicious messages and not filtering too many messages so that spam still gets into the inboxes.
N What are the problems to be solved ?We need a spam filtering solution to prevent our users from becoming victims of fraud while improving the user experience. We will discuss these indicators in detail in the following sections. We will look at two performance metrics to measure our success: accuracy and recall. To solve this problem, we will let our ML model learn from the original e-mail dataset and use the subject line to classify suspicious e-mails as spam. We want to filter out suspicious email, but at the same time, we don’t want to filter too much, so that non spam goes into the spam folder and will never be seen by users. However, it is difficult to have the right spam filtering solution. Spam filtering technology is an important step for e-mail service to avoid users suffering from such crimes. Spam can also be used to obtain personal data, which can then be used for identity theft and various other crimes. For example, spam can be designed to obtain credit card number or bank account information that can be used for credit card fraud or money laundering. In addition to this, spam may bring more risks.
We may already be familiar with spam spam filtering is a basic function of mass email services. Let’s start by defining the issues to be addressed in this chapter. L Validate classification model Definition problem L Logical regression and naive Bayesian Email Spam filtering In this chapter, we will discuss the following topics: This will help us understand the workflow. We will begin to follow the steps for developing ml models discussed in the previous chapter. We will use a raw email dataset that contains both spam and non spam and use it to train our ML model. In this chapter, we will build a spam filtering classification model.