Building a Credit Card Fraud Detection Model with ML

Building a Credit Card Fraud Detection Model with ML

Table of Contents

• Introduction
• Project Overview
• Data Import and Exploration
• Data Preprocessing
• Building the Logistic Regression Model
• Creating the Web App
• Testing the App
• Conclusion
• References

Introduction

In this article, we will explore the process of building a credit card fraud detection model using machine learning techniques. We will start by understanding the dataset and its features, then proceed to preprocess the data and build a logistic regression model. Finally, we will create a web application to showcase the fraud detection capabilities of the model.

Project Overview

The goal of this project is to develop a machine learning model that can accurately identify fraudulent credit card transactions. We will be using a dataset that contains various features related to credit card transactions, such as transaction amount, transaction location, and time elapsed between transactions.

Data Import and Exploration

To begin, we will import the necessary packages and load the dataset. The dataset contains a total of 284,807 entries with 31 features. We will examine the distribution of legitimate and fraudulent transactions in the dataset, and observe that it is an imbalanced dataset.

Data Preprocessing

To address the issue of imbalanced data, we will perform data balancing by randomly undersampling the majority class (legitimate transactions) to match the number of samples in the minority class (fraudulent transactions). This will ensure that our model is trained on a balanced dataset.

Building the Logistic Regression Model

Next, we will create an object of logistic regression and train the model using the balanced dataset. Logistic regression is a classification algorithm that is commonly used for binary classification problems like fraud detection. We will explain the working principle of logistic regression and its suitability for our problem.

Creating the Web App

Once the model is trained, we will create a web application to provide a user-friendly interface for detecting credit card fraud. The web app will allow users to input transaction details and predict whether the transaction is legitimate or fraudulent. We will use the Flask framework to develop the web app.

Testing the App

To ensure the app is functioning correctly, we will perform testing by entering different transaction details and verifying the predictions made by the model. We will discuss the importance of testing and how it helps to validate the accuracy of the fraud detection model.

Conclusion

In conclusion, this project demonstrates the process of building a credit card fraud detection model using machine learning techniques. By leveraging logistic regression and a balanced dataset, we are able to accurately identify fraudulent transactions. The web app provides an intuitive interface for users to detect fraud in real-time transactions.

References

1. Credit Card Fraud Detection Dataset: Link