Credit Card Fraud Detection Web App

This repository contains a complete machine learning project for detecting fraudulent credit card transactions. It includes data preprocessing, model training, evaluation, and a web application for real-time predictions.Detecting credit card fraud is a high-stakes challenge that blends data science, cybersecurity, and real-time analytics. Here’s a breakdown of the key skills used in building and deploying fraud detection systems:

Specification about what I used and achieved.

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Model Training

• Architecture

  • Logistic Regression with balanced class weight.

• Inference Results

  • Accuracy: 0.976
  • Recall: 0.896

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Web App Production

• Procfile
  • Contains the type of app.
• Requirements
  • Libraries needed to run the app.
• Templates
  • Files required for rendering purpose
• Static
  • CSS styles
• App
  • Main file which will run our Web App.

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Report

A thorough report on what can be found in FinalReport.md or FinalReport.pdf file.

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Test Data for Fraud Transaction:

Testing data for fraud transaction can be found in the "fraud_values.csv" file.

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Test Data for Valid Transaction:

Testing data for a Valid transaction can be found in the "valid_values.csv" file.

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