Title:
Credit card fraud detection with machine learning | Step-by-step guide

Description:

Welcome to our in-depth tutorial on credit card fraud detection using machine learning! In this video, we will walk you through the entire process of building and evaluating different machine learning models to detect fraudulent transactions. Whether you are a beginner or an experienced data scientist, this guide will help you understand the key concepts and steps.

Project overview:

In this project, we use a dataset of credit card transactions to identify fraudulent activity. We examine the data, pre-process it, and then train several machine learning models, including:

Artificial neural networks (ANN)
XGBoost
Random Forest
Cat boost
LightGBM
We evaluate these models on metrics such as accuracy, precision, recall and F1 score and compare their performance to find the best model for fraud detection.

Resources:

Dataset: Credit card fraud detection dataset on Kaggle
Project Code: GitHub Repository – https://github.com/Shravani-kurkute/Prasunethon-Hackathon/blob/main/Credit_card.ipynb
Libraries used: Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, XGBoost, CatBoost, LightGBM.

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