https://github.com/das-amlan/detecting-credit-card-fraud

Detecting Credit Card Fraud -- For practicing R markdown language and implementing different ML algorithm to train a fraud detection model.
https://github.com/das-amlan/detecting-credit-card-fraud

decision-tree gbm gradient-boosting logistic-regression machine-learning r rmarkdown rprogramming

Last synced: 3 days ago
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Detecting Credit Card Fraud -- For practicing R markdown language and implementing different ML algorithm to train a fraud detection model.

• Host: GitHub
• URL: https://github.com/das-amlan/detecting-credit-card-fraud
• Owner: das-amlan
• Created: 2022-08-23T14:51:52.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-04T00:59:50.000Z (almost 2 years ago)
• Last Synced: 2023-05-29T10:12:49.569Z (over 1 year ago)
• Topics: decision-tree, gbm, gradient-boosting, logistic-regression, machine-learning, r, rmarkdown, rprogramming
• Homepage:
• Size: 290 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Detecting Credit Card Fraud

## Overview

This project focuses on the development of a credit card fraud detection model using machine learning algorithms. The goal of this project is to build a classifier that can accurately identify fraudulent transactions. The implementation of the project is done using the R programming language.

## Algorithms Used

In this project, the following machine learning algorithms were used:

* Logistic Regression

* Decision Tree

* Gradient Boosting Classifier (GBM)

## Evaluation Metric

The model was evaluated based on Receiver Operating Characteristic (ROC) curve.

## R Markdown

The project was also an opportunity to learn R Markdown for presenting findings. R Markdown makes the code more readable and explainable to others and provides an efficient way to document the results.

### Why R Markdown

* Reproducibility: Easily reproduce results for others or for future reference.

* Efficient Documentation: Streamlines documentation of analysis, including code and output, in one place.

* Readability: Uses markdown syntax for easy formatting and better understanding of results.

* Dynamic outputs: Includes dynamic output such as plots and tables that automatically update as code changes.

* Valuable tool: Provides an efficient, reproducible, and readable way to document and present data analysis results.

## Conclusion

This project provided a hands-on experience in the development of a model for the detection of credit card fraud using machine learning algorithms in R. The results demonstrate the potential of these algorithms for the effective identification of fraudulent transactions.

[Credit: DataFlair](https://data-flair.training/blogs/data-science-machine-learning-project-credit-card-fraud-detection/)