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We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software infracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labelled or unlaballed data provided as a relational table in csv format. ==We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software in fracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format. * Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing ==* Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018, Springer International Publishing
| With businesses moving online, fraud, and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving, and maintainable. Importantly, they can scale with the online business. | ||
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| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software infracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labelled or unlaballed data provided as a relational table in csv format. | ||
| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software in fracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format. |
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon | ||
| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE | ||
| * Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) | ||
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-All; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon |
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Hello @microsoft871 , thank you for your suggestions to improve the README. Please correct the proposed changes in the comments in order to get this PR accepted.
| Both of the trained models are deployed to Amazon SageMaker managed real-time endpoints that host the models and can be invoked to provide model predictions for new transactions. | ||
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| The model training and endpoint deployment is orchestrated by running a [jupyter notebook](source/notebooks/sagemaker_fraud_detection.ipynb) on a SageMaker Notebook instance. The jupyter notebook runs a demonstration of the project using the aforementioned anonymized credit card dataset that is automatically downloaded to the Amazon S3 Bucket created when you launch the solution. However, the notebook can be modified to run the project on a custom dataset in S3. The notebook instance also contains some example code that shows how to invoke the REST API for inference. | ||
| The model training and endpoint deployment is orchestrated by running a [jupyter notebook](source/notebooks/sagemaker_fraud_detection.ipynb) on a SageMaker Notebook instance. The Jupiter notebook runs a demonstration of the project using the aforementioned anonymized credit card dataset that is automatically downloaded to the Amazon S3 Bucket created when you launch the solution. However, the notebook can be modified to run the project on a custom dataset in S3. The notebook instance also contains some example code that shows how to invoke the REST API for inference. |
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| The model training and endpoint deployment is orchestrated by running a [jupyter notebook](source/notebooks/sagemaker_fraud_detection.ipynb) on a SageMaker Notebook instance. The Jupiter notebook runs a demonstration of the project using the aforementioned anonymized credit card dataset that is automatically downloaded to the Amazon S3 Bucket created when you launch the solution. However, the notebook can be modified to run the project on a custom dataset in S3. The notebook instance also contains some example code that shows how to invoke the REST API for inference. | |
| The model training and endpoint deployment is orchestrated by running a [jupyter notebook](source/notebooks/sagemaker_fraud_detection.ipynb) on a SageMaker Notebook instance. The Jupyter notebook runs a demonstration of the project using the aforementioned anonymized credit card dataset that is automatically downloaded to the Amazon S3 Bucket created when you launch the solution. However, the notebook can be modified to run the project on a custom dataset in S3. The notebook instance also contains some example code that shows how to invoke the REST API for inference. |
| The model training and endpoint deployment is orchestrated by running a [jupyter notebook](source/notebooks/sagemaker_fraud_detection.ipynb) on a SageMaker Notebook instance. The Jupiter notebook runs a demonstration of the project using the aforementioned anonymized credit card dataset that is automatically downloaded to the Amazon S3 Bucket created when you launch the solution. However, the notebook can be modified to run the project on a custom dataset in S3. The notebook instance also contains some example code that shows how to invoke the REST API for inference. | ||
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| In order to encapsulate the project as a stand-alone microservice, Amazon API Gateway is used to provide a REST API, that is backed by an AWS Lambda function. The Lambda function runs the [code](https://github.com/awslabs/fraud-detection-using-machine-learning/blob/master/source/fraud_detection/index.py) to preprocess incoming transactions, invoke sagemaker endpoints, merge results from both endpoints if necessary, store the model inputs and model predictions in S3 via Kinesis Firehose, and provide a response to the client. | ||
| To encapsulate the project as a stand-alone microservice, Amazon API Gateway is used to provide a REST API, that is backed by an AWS Lambda function. The Lambda function runs the [code](https://github.com/awslabs/fraud-detection-using-machine-learning/blob/master/source/fraud_detection/index.py) to preprocess incoming transactions, invoke Pagemaker endpoints, merge results from both endpoints if necessary, store the model inputs and model predictions in S3 via Kinesis Firehose, and provide a response to the client. |
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| To encapsulate the project as a stand-alone microservice, Amazon API Gateway is used to provide a REST API, that is backed by an AWS Lambda function. The Lambda function runs the [code](https://github.com/awslabs/fraud-detection-using-machine-learning/blob/master/source/fraud_detection/index.py) to preprocess incoming transactions, invoke Pagemaker endpoints, merge results from both endpoints if necessary, store the model inputs and model predictions in S3 via Kinesis Firehose, and provide a response to the client. | |
| To encapsulate the project as a stand-alone microservice, Amazon API Gateway is used to provide a REST API, that is backed by an AWS Lambda function. The Lambda function runs the [code](https://github.com/awslabs/fraud-detection-using-machine-learning/blob/master/source/fraud_detection/index.py) to preprocess incoming transactions, invoke Sagemaker endpoints, merge results from both endpoints if necessary, store the model inputs and model predictions in S3 via Kinesis Firehose, and provide a response to the client. |
| The dataset contains credit card transactions from European cardholders in 2013. As is common in fraud detection, | ||
| it is highly unbalanced, with 492 fraudulent transactions out of the 284,807 total transactions. The dataset contains | ||
| only numerical features, because the original features have been transformed for confidentiality using PCA. As a result, | ||
| only numerical features because the original features have been transformed for confidentiality using PCA. As a result, |
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| only numerical features because the original features have been transformed for confidentiality using PCA. As a result, | |
| only numerical features, because the original features have been transformed for confidentiality using PCA. As a result, |
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon | ||
| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE | ||
| * Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) | ||
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-All; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon |
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| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-All; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon | |
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Aël; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon |
| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE | ||
| * Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) | ||
| * Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-All; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon | ||
| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018, IEEE |
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| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018, IEEE | |
| * Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: A realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018, IEEE |
| With businesses moving online, fraud, and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving, and maintainable. Importantly, they can scale with the online business. | ||
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| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software infracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labelled or unlaballed data provided as a relational table in csv format. | ||
| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software in fracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format. |
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| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software in fracture. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format. | |
| Specifically, we show how to use Amazon SageMaker to train supervised and unsupervised machine learning models on historical transactions, so that they can predict the likelihood of incoming transactions being fraudulent or not. We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software infrastructure. This project includes a demonstration of this process using a public, anonymized credit card transactions [dataset provided by ULB](https://www.kaggle.com/mlg-ulb/creditcardfraud), but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format. |
| # Fraud Detection using Machine Learning | ||
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| With businesses moving online, fraud and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving and maintainable. Importantly, they can scale with the online business. | ||
| With businesses moving online, fraud, and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving, and maintainable. Importantly, they can scale with the online business. |
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| With businesses moving online, fraud, and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving, and maintainable. Importantly, they can scale with the online business. | |
| With businesses moving online, fraud and abuse in online systems is constantly increasing as well. Traditionally, rule-based fraud detection systems are used to combat online fraud, but these rely on a static set of rules created by human experts. This project uses machine learning to create models for fraud detection that are dynamic, self-improving, and maintainable. Importantly, they can scale with the online business. |
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We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software infracture. This project includes a demonstration of this process using a public, anonymized credit card transactions dataset provided by ULB, but can be easily modified to work with custom labelled or unlaballed data provided as a relational table in csv format. ==We also show how to deploy the models, once trained, to a REST API that can be integrated into an existing business software in fracture. This project includes a demonstration of this process using a public, anonymized credit card transactions dataset provided by ULB, but can be easily modified to work with custom labeled or unlabeled data provided as a relational table in CSV format.
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