A simple classification problem - that of labelling emails as spam or non-spam, based on their content in terms of words using two machine learning models based on K Nearest Neighbors (KNN) and Decision Trees

One of the most widely utilized ways of communication in the modern day is email. Regrettably, email spam is an issue that keeps getting worse. Spam emails are unsolicited messages that are sent to many people and frequently with the intention of making a sale. Machine learning algorithms can be used for the task of recognizing spam emails. In this study, we will investigate the usage of KNN and Decision Tree models to identify emails as spam or not.

The dataset used for this project is taken from the UCI Machine Learning Repository. It is made up of 4601 emails that have been pre-processed into 57 characteristics that indicate different aspects of the emails, such as the frequency of particular words, characters, and punctuation marks. Spam and non-spam are the two subcategories of the data. The collection contains 2788 non-spam emails and 1813 spam emails.