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This framework helps to accelerate data pipelining, enhance data science prototyping, and promote pipeline reproducibility.)\n\n## Motivation\n- Explore how unsupervised anomaly detection works, and better understand the concept and implementation of isolation forest\n- Leverage Kedro framework to optimally structure data science pipeline projects\n\n## Data\nThe [credit card transaction data](https://github.com/Fraud-Detection-Handbook/simulated-data-transformed) is obtained from the collaboration between Worldline and Machine Learning Group. It is a realistic simulation of real-world credit card transactions and has been designed to include complicated fraud detection issues.\n\n## General Pipeline Structure\n![Alt text](/docs/images/01_DS_Pipeline_Overview.png?raw=true)\n\n## Anomaly Detection Pipeline Structure\n![Alt text](/docs/images/05_Anomaly_Detection_Pipeline_Blueprint.png?raw=true)\n\n## Steps\n1. Change path to project directory in command line - `cd C:/Anomaly-Detection-Pipeline-Kedro`\n2. Initialize Conda virtual environment (create one if not done so) - `conda activate env_kedro`\n3. Execute a pipeline run with `kedro run`\n\nPlease see the [walkthrough article](https://neptune.ai/blog/data-science-pipelines-with-kedro) for details\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkennethleungty%2FAnomaly-Detection-Pipeline-Kedro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkennethleungty%2FAnomaly-Detection-Pipeline-Kedro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkennethleungty%2FAnomaly-Detection-Pipeline-Kedro/lists"}