I completed this project as part of my Introduction to Data Science Class at Loyola Chicago. I used a dataset titled "Fraudulent E-Commerce Transactions" from Kaggle. The output can be found here.
Note The "index.html" file houses the typical .html file for this project. I had to rename it to index to host it on Github Pages.
I will be investigating factors that contribute to fraudulent transactions, such as user age, payment method, product type, and account age. I will likely strip some of the variables that are unnecessary, such as location, device used, and ip address as they don’t have much bearing on the analysis.
What factors lead to fraudulent transactions? Are younger people more likely to be scammed than older people? Is there a certain type of item that is frequently fraudulently sold? Are certain payment methods more fraud-resistant? Does price matter, or are larger purchases scams as frequently as smaller ones?
The project report can be found in this repository, as well as on my Linkedin.