Credit_Card_Fraud

Data is available in https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

Credit Card Fraud Detection using Logistic Regression

This Python script demonstrates credit card fraud detection using Logistic Regression. It loads a dataset, preprocesses it, trains a Logistic Regression model, and evaluates its performance.

Requirements

• Python 3.x
• pandas
• scikit-learn
• matplotlib

Usage

1. Ensure you have the required libraries installed (pandas, scikit-learn, matplotlib).
2. Save your dataset as creditcard.csv in the same directory as the script.
3. Run the script in a Python environment.

Description

• Loading the Dataset: The script loads the credit card transaction dataset from creditcard.csv.

• Preprocessing: It separates features (X) and the target variable (y) and then splits the dataset into training and testing sets. It also standardizes the features using StandardScaler.

• Model Training: It creates a Logistic Regression model and fits it on the training data.

• Evaluation: The model's performance is evaluated using the ROC AUC score. It also plots a precision-recall curve to visualize the precision-recall trade-off.