Credit Card Fraud Detection

Overview

This project focuses on detecting fraudulent credit card transactions using machine learning. The dataset contains transactions made by European cardholders in September 2013. The goal is to build a model that can accurately classify transactions as either fraudulent or non-fraudulent.

Dataset

The dataset used in this project is the Credit Card Fraud Detection dataset, which can be found on Kaggle. It contains 284,807 transactions, of which 492 are fraudulent. The dataset is highly imbalanced, with frauds accounting for only 0.172% of all transactions.

Features

• V1-V28: Principal components obtained through PCA (anonymized for privacy reasons).
• Amount: The transaction amount.
• Class: The target variable (1 for fraud, 0 for non-fraud).

Project Steps

1. Exploratory Data Analysis (EDA):

   • Analyzed the distribution of classes (fraud vs. non-fraud).
   • Visualized the features to understand their behavior.

2. Data Preprocessing:

   • Split the dataset into training and testing sets (70% training, 30% testing).

3. Model Training:

   • Trained a Logistic Regression model to classify transactions.

4. Model Evaluation:

   • Evaluated the model using a confusion matrix, F1 score, and recall.

Results

Confusion Matrix

Evaluation Metrics

• F1 Score: 0.85
• Recall (Sensitivity): 0.82

The model performs well in detecting fraudulent transactions, with a high recall score indicating that it captures most of the fraud cases.

How to Run the Code

1. Clone this repository:

   git clone https://github.com/your-username/credit-card-fraud-detection.git