Credit-Card-Fraud-Detection

Credit card companies must accurately detect fraudulent transactions to protect customers from unauthorized charges. This project focuses on developing a machine learning model to identify such fraudulent transactions.

The dataset used in this project contains credit card transactions made by European cardholders in September 2013. It includes transactions from a span of two days, with a total of 284,807 transactions, out of which 492 are identified as fraudulent. This makes the dataset highly imbalanced, with frauds accounting for only 0.172% of all transactions.

Get Started with

https://github.com/Ruban-Edward/Credit-Card-Fraud-Detection

Dataset Description

The dataset consists of numerical input variables resulting from a Principal Component Analysis (PCA) transformation. Due to confidentiality, the original features and additional background information are not provided. The dataset includes the following features:

• V1, V2, ..., V28: Principal components obtained through PCA.
• Time: The seconds elapsed between each transaction and the first transaction in the dataset.
• Amount: The transaction amount, which can be used for cost-sensitive learning.
• Class: The response variable, taking the value 1 for fraud and 0 otherwise.

Project Structure

• notebooks : Jupyter notebooks for data exploration, preprocessing, and model training.
• README.md : Project documentation.

Installation

To run this project locally, follow these steps:

1. Clone this Repository :

   git clone https://github.com/Ruban-Edward/Credit-Card-Fraud-Detection

2. Install Python :

   Arch Linux

   sudo pacman -S python

   Windows

   https://www.python.org/downloads/

3. Create a virtual environment :

   python -m venv venv
   source venv/bin/activate

4. Install required pip libraries

   • numpy
   • pandas
   • matplotlib
   • seaborn
   • scikit-learn

   pip install <above packages name>

5. Open the Jupyter notebook to run the Project

   jupyter notebook

Evaluation Metrics

• Precision: The proportion of true positive predictions among all positive predictions.
• Recall: The proportion of true positive predictions among all actual positives.
• F1-Score: The harmonic mean of precision and recall.
• ROC-AUC: The area under the receiver operating characteristic curve.

Acknowledgements

Download the Dataset from the Kaggle

https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

Licence

MIT license