Credit Card Fraud Detection Model

This repository contains a credit card fraud detection model trained using logistic regression. The model aims to classify transactions as either fraudulent or legitimate based on features extracted from the provided dataset.

Dataset The dataset used for this project is sourced from Kaggle: Credit Card Fraud Detection. It contains a labeled set of credit card transactions, with features anonymized for privacy reasons.

Requirements Python 3.x pandas numpy scikit-learn (sklearn)

Usage Clone this repository: bash Copy code git clone https://github.com/izuasomba/credit-card-fraud-detection.git cd credit-card-fraud-detection Install the required dependencies:

Copy code Download the dataset from the provided link and place it in the data/ directory.

Run the Jupyter Notebook or Python script:

Copy code jupyter notebook credit_card_fraud_detection.ipynb or

Copy code python credit_card_fraud_detection.py

Model The logistic regression algorithm is used for this binary classification problem. The model is trained on the provided dataset and aims to predict whether a given transaction is fraudulent or not.

Results The model's performance is evaluated using standard classification metrics such as accuracy, precision, recall, and F1-score. The goal is to effectively detect fraudulent transactions while minimizing false positives.

License This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments The dataset used in this project is provided by the ULB Machine Learning Group on Kaggle.