CodTech-Task2

Credit Card Fraud Detection

This repository contains the code for detecting credit card fraud using Logistic Regression and KMeans clustering approaches. The project aims to build models that can identify fraudulent transactions with high accuracy.

Table of Contents

• Introduction
• Approaches
  • Logistic Regression
  • KMeans Clustering
• Dataset

Introduction

Credit card fraud is a significant problem in the financial industry. This project implements two machine learning approaches to detect fraudulent transactions:

1. Logistic Regression
2. KMeans Clustering

Approaches

Logistic Regression

Logistic Regression is a supervised learning algorithm used for binary classification. It models the probability that a given input point belongs to a certain class.

KMeans Clustering

KMeans is an unsupervised learning algorithm used for clustering. It partitions the data into K clusters, aiming to minimize the within-cluster variance. In this context, it is used to identify patterns that differentiate fraudulent transactions from non-fraudulent ones.

Dataset

The dataset used in this project is the Credit Card Fraud Detection dataset from Kaggle. It contains transactions made by European cardholders in September 2013. The dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.