Credit card fraud detection

Table of Contents

• Introduction
• Data Description
• Methods Used
• Technology Stack

Introduction

Project made in Jupyter Notebook with Kaggle Credit Card Fraud Detection Dataset 2023, which aims at selection of best supervised machine learning model for capturing credit card frauds.

Data Description

Dataset contains credit card transactions made by European cardholders in the year 2023. It comprises over 550,000 records, and the data has been anonymized to protect the cardholders' identities. It consists of 28 anonymized feauters (V1,V2,V3, up to V28), accompanied by amout of the transaction and class feauture, which is a binary label indicating whether the transaction is fraudulent (1) or not (0).

Methods used

• Data Visualization
• Supervised Machine Learning Alogorithms: Logistic Regression, Random Forest, K-nearest Neighbors, Decision Trees, Naive Bayes.

Technology stack

• Python 3.9.18
• Pandas 2.0.3
• Numpy 1.26.0
• Matplotlib 3.8.0
• Scikit-Learn 1.3.1
• Seaborn 0.13.0