💳 Credit Card Fraud Detection

This project implements a machine learning-based Credit Card Fraud Detection System using Logistic Regression. It effectively detects fraudulent transactions with high accuracy using real-world anonymized transaction data.

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Overview

This project aims to detect fraudulent credit card transactions using statistical preprocessing and logistic regression. The model achieves up to 93% accuracy on the test dataset and is a lightweight, reliable baseline for fraud detection tasks.

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Features

• Machine Learning Model: Trained using LogisticRegression from scikit-learn
• High Accuracy: Achieves ~93% test accuracy
• Data Preprocessing: Includes downsampling, normalization, and correlation filtering
• Fraud Detection: Helps improve financial security through fraud prediction

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Dataset

The dataset is obtained from Kaggle: Credit Card Fraud Detection. It contains transactions made by European cardholders in September 2013 and is heavily imbalanced (0.17% fraud).

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Getting Started

1. Clone the Repository

git clone https://github.com/prerana2005/Credit_Card_Fraud_Detection

2. Install the required dependencies

pip install -r requirements.txt