FDS_Final_Project

The project involves analyzing transactions and classifying them into fraudulent and non-fraudulent. The data set we worked on is this.

Content

• main.ipynb : the jupyter notebook with the EDA, Feature engineering, model selection and reported metrics;
• 11_tamburini_mandara_gheorghiu_dipoce_grimaldi.pdf : the initial presentation of our group preject;
• 28_tamburini_mandara_gheorghiu_dipoce_grimaldi.pdf : the final presentation of the project with the analysis of our learning process and our results;
• Tamburini_Mandara_Gheorghiu_Grimaldi_DiPoce_report.pdf : the "formal report" of our project.

Used tools:

• python libraries: pandas, scikit learn, matplotlib.pyplot;
• jupyter notebook.