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https://github.com/ruban-edward/credit-card-fraud-detection
The increase in credit card fraud brought on by weaknesses in the system. We employ machine learning algorithms such as Logistic Regression, Decision Trees and Support Vector Machine. The accuracy results in detecting fraudulent transactions appears promising.
https://github.com/ruban-edward/credit-card-fraud-detection
credit-card-fraud credit-card-fraud-detection decision-tree jupyter-notebook logistic-regression machine-learning support-vector-machine
Last synced: 24 days ago
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The increase in credit card fraud brought on by weaknesses in the system. We employ machine learning algorithms such as Logistic Regression, Decision Trees and Support Vector Machine. The accuracy results in detecting fraudulent transactions appears promising.
- Host: GitHub
- URL: https://github.com/ruban-edward/credit-card-fraud-detection
- Owner: Ruban-Edward
- License: mit
- Created: 2024-03-15T06:13:58.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-06-15T16:48:06.000Z (8 months ago)
- Last Synced: 2024-11-18T02:13:04.696Z (3 months ago)
- Topics: credit-card-fraud, credit-card-fraud-detection, decision-tree, jupyter-notebook, logistic-regression, machine-learning, support-vector-machine
- Language: Jupyter Notebook
- Homepage:
- Size: 222 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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
```bash
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** :
```bash
git clone https://github.com/Ruban-Edward/Credit-Card-Fraud-Detection
```
2. **Install Python** :
Arch Linux
```bash
sudo pacman -S python
```
Windows
```bash
https://www.python.org/downloads/
```
4. **Create a virtual environment** :
```bash
python -m venv venv
source venv/bin/activate
```
5. **Install required pip libraries**
* numpy
* pandas
* matplotlib
* seaborn
* scikit-learn
```bash
pip install
```
6. Open the Jupyter notebook to run the Project
```bash
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](LICENSE)