Credit Card Fraud Detection

Credit Card Fraud Detection

Project Description

Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent.

Overview

Data Modeling & Analysis

Logistic Regression

Random Forests

Decision Trees

Feed-Forward Neural Network

Language & Tools

Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Keras)

Featured Notebook

Credit Card Fraud Detection

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GitHub - dpghazi/credit-card-fraud-detection: Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent.

Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent. - GitHub - dpghazi/credit-card-fraud-detection: Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent.

GitHub - dpghazi/credit-card-fraud-detection: Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent.

https://github.com/dpghazi/credit-card-fraud-detection

GitHub - dpghazi/credit-card-fraud-detection: Examined credit card transactions by European card holders, and created machine learning models (logistic regression, random forests, and feed-forward neural network) to classify a given transaction as fraudulent or non-fraudulent.