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Throughout the financial sector, machine learning algorithms are being developed to detect fraudulent transactions. In this project, that is exactly what we are going to be doing as well. Using a dataset of of nearly 28,500 credit card transactions and multiple unsupervised anomaly detection algorithms, we are going to identify transactions with a high probability of being credit card fraud. In this project, we will build and deploy the following two machine learning algorithms:

License: GNU General Public License v3.0

Jupyter Notebook 100.00%

credit-card-fraud-detection's Introduction

Credit-Card-Fraud-Detection

Throughout the financial sector, machine learning algorithms are being developed to detect fraudulent transactions. In this project, that is exactly what we are going to be doing as well. Using a dataset of of nearly 28,500 credit card transactions and multiple unsupervised anomaly detection algorithms, we are going to identify transactions with a high probability of being credit card fraud. In this project, we will build and deploy the following two machine learning algorithms:

  • Local Outlier Factor (LOF)
  • Isolation Forest Algorithm

USING JUPYTER NOTEBOOK

Download Data Set here - Download Data from Kaggle

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