A Machine learning model for credit card fraud detection for improved financial security This project aims to develop a system that can detect credit card frauds using machine learning algorithms and provide a web-based user interface for users to report any suspicious activities.
Detects fraudulent transactions using machine learning algorithms Provides a user-friendly web interface for reporting suspicious activities
Python Flask Scikit-Learn Pandas NumPy JavaScript HTML/CSS
Clone repository and install necessary packages
python API.py This will start the Flask web server and the user interface can be accessed by navigating to http://localhost:5000 in a web browser.
The dataset used in this project is the Credit Card Fraud Detection dataset from Kaggle, which contains credit card transactions made over a two-day period in September 2013 by European cardholders. Here's a link to the dataset https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud.
Dataset folder
We used a Logistic Regression to detect fraudulent transactions. The model was trained on the Credit Card Fraud Detection dataset and achieved an accuracy of 94.4%.
Carolyne Ndunge(project Lead) Mrs. Vivian Aloo (Project Supervisor)
This project is licensed under the MIT License - see the LICENSE file for details.