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Credit Card Fraud Detection using Machine Learning (ML) with OneAPI

Rithvika T

Rithvika T

Bengaluru, Karnataka

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  • 0 Collaborators

Credit card fraud detection using oneAPI leverages machine learning to detect fraudulent transactions, enabling secure financial transactions. Enhances fraud detection accuracy and efficiency with Intel's powerful oneAPI framework. Protects users from financial losses. ...learn more

Project status: Published/In Market

oneAPI, Artificial Intelligence

Intel Technologies
oneAPI

Code Samples [1]

Overview / Usage

This project focuses on developing a credit card fraud detection system using machine learning techniques and oneAPI. The goal is to solve the problem of fraudulent transactions by analyzing patterns and anomalies in credit card data. By leveraging advanced algorithms and predictive models, the system can accurately identify and flag suspicious activities in real-time, mitigating financial losses for individuals and organizations. The work/research conducted in this project has practical applications in the financial industry, where it can be deployed in production to enhance fraud detection capabilities and protect users from fraudulent transactions.

Methodology / Approach

In this project, we are using machine learning techniques and oneAPI to develop a credit card fraud detection system. The methodology involves the following steps:

Data Preprocessing: We preprocess the credit card transaction data by performing data cleaning, handling missing values, and scaling the features.

**Feature Engineering: **We extract relevant features from the data that can help distinguish between legitimate and fraudulent transactions. This includes analyzing transaction metadata, transaction amounts, timestamps, and other relevant information.

Model Development: We train machine learning models, such as logistic regression, decision trees, random forests, or deep learning models, using the preprocessed data. These models learn patterns and anomalies in the data to identify fraudulent transactions.

Model Evaluation: We evaluate the performance of the trained models using appropriate metrics such as accuracy, precision and recall. This helps us assess the effectiveness of the models in detecting fraud.

Deployment and Integration: We integrate the trained models into a production-ready system using oneAPI. This allows for efficient execution on Intel architectures, ensuring scalability and performance.

Technologies Used

  • **Technologies: **Machine Learning, Deep Learning
  • Libraries: scikit-learn, pandas, numpy
  • Tools: Jupyter Notebook, Python
  • Software: Intel oneAPI, Intel Distribution for Python
  • **Hardware: **CPU, GPU

Repository

https://github.com/rithvika7495/Credit-Card-Fraud-Detection-OneAPI

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