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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Autoencoder for Fraud Detection
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By: FraudML Latest Version: v1

Product Overview

This deep autoencoder is trained using TensorFlow and seeks to provide greater performance when compared to traditional anomaly detection techniques. The deep autoencoder returns a probability score, which corresponds to the probability that the customer/transaction is fraudulent. The algorithm trains a deep autoencoder model to predict on classification datasets directly from CSV data. The algorithm can save time by automating time-consuming manual steps such as feature engineering and feature selection. It can detect anomalies in highly-imbalanced data sets. The algorithm is ideally used to detect anomalies in transactional data

Key Data

Version
By FraudML
Categories
Type Algorithm
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Additional Resources

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Support Information

Autoencoder for Fraud Detection

For any issues please feel free to contact us at info@fraudml.com

www.fraudml.com

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