Making the Pixels Visible

Websites are one of the most important components of the internet. We use web browsers every day to browse websites using the internet for searching for information, buying goods, reading news, following online lectures, interacting on social media, etc. When a website is opened, it contains many components that are too complex for an ordinary user to be aware of. By using third-party tracking cookies or pixels added for advertising or analytical purposes, ad networks can track the user on the web. As browser extensions that use filter lists became more successful at blocking cookies, followers began to find alternative ways to identify users. One way for followers to identify individuals is to use invisible pixels. In this project, my goal is to perform exploratory data analysis and feature engineering of the dataset, using a previously created invisible pixel dataset, and eventually create a machine learning model that classifies these pixels and convert this machine learning model to suitable form to be used in a browser extension that can be developed.