Description

Python Machine Learning Project for Detecting Credit Card Fraud

Today, we will delve into the world of the Credit Card Fraud Detection System Machine Learning Project. This project aims to uncover fraudulent activities related to credit card transactions. Various methods such as decision trees, logistic regression, artificial neural networks, and gradient boosting classifiers will be explored. The project will utilize the Card Dealings dataset, which contains both fraudulent and non-fraudulent transactions.

The main goal of the Credit Card Fraud Detection System Machine Learning Project is to create a classifier that can accurately identify fraudulent credit card transactions. By employing different machine learning techniques, the project aims to differentiate between fraudulent and non-fraudulent data. Upon completion of this project, users will gain valuable insights into the application of machine learning algorithms for classification purposes.

Key Features:

• The system stores previous transaction patterns for each user.
• It calculates user characteristics based on expenditure ability and location.
• Any transaction with a 20-30% deviation from the user’s usual behavior is flagged as suspicious.

With these features in place, you are now equipped to detect and prevent fraud. The Credit Card Fraud Detection System Machine Learning Project is a crucial technology of our time and is expected to remain relevant indefinitely. Students can utilize this project for their academic needs at no cost.

Algorithms Used for Fraud Detection:

• Logistic Regression
• Random Forest

Static Pages and Additional Sections:

The following static pages are included in the Credit Card Fraud Detection System project:

• Home Page with an appealing UI design
• An animated slider for image banners on the Home Page
• About Us page providing information about the project
• Contact Us page for user inquiries

Technologies Utilized in the Project:

This project was developed using the following technologies:

• HTML: Page layout designed in HTML
• CSS: Styling implemented using CSS
• JavaScript: Validation tasks and animations developed with JavaScript
• Python: Business logic implemented in Python
• MySQL: Database management using MySQL
• Django: Project built on the Django Framework

Supported Operating Systems:

This project can be configured on the following operating systems:

• Windows: Easily set up on Windows OS by installing Python, PIP, and Django.
• Linux: Compatible with all versions of Linux operating systems.
• Mac: Can be configured on Mac operating systems as well.