This project aims to detect fraudulent credit card transactions using a Feedforward Deep Neural Network (FDNN). The dataset used for this project is the well-known Credit Card Fraud Detection dataset, which contains transactions made by credit cards in September 2013 by European cardholders.
Credit card fraud is a significant issue in the financial industry, and early detection is crucial to prevent substantial financial losses. This project implements a Feedforward Deep Neural Network (FDNN) to classify transactions as fraudulent or legitimate based on various features.
The dataset used in this project is sourced from Kaggle [https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud] and contains the following features:
Time: Number of seconds elapsed between this transaction and the first transaction in the dataset.V1, V2, ..., V28: Principal components obtained using PCA to protect user identities and sensitive features.Amount: Transaction amount.Class: Class label (0 for legitimate, 1 for fraudulent).
You can install these dependencies using pip:
pip install pandas numpy matplotlib scikit-learn tensorflow keras
- Clone this repository:
https://github.com/madhavgn007/Credit-Card-Fraud-Detection-using-FDNN.git
- Navigate to the project directory:
cd Credit-Card-Fraud-Detection-using-FDNN
-
Place the
creditcard.csvdataset in the project directory. -
Open the Jupyter Notebook
FDNN.ipynb -
Run the notebook cells to train the model and evaluate its performance.
The performance of the model is evaluated using metrics such as:
- Accuracy
- Precision
- Recall
- F1-Score
- ROC-AUC Score
These metrics provide a comprehensive evaluation of the model's ability to detect fraudulent transactions accurately.
Contributions to this project are welcome. You can contribute by:
- Reporting bugs
- Suggesting new features
- Writing or improving - documentation
- Submitting pull requests
Please ensure that your contributions align with the project's coding style and standards.