type

Author: atqy

end_to_end/fraud_detection/1-data-prep-e2e.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "## Background\n",
     "\n",
-    "This notebook is the second part of a series of notebooks that will demonstrate how to prepare, train, and deploy a model that detects fradulent auto claims.In this notebook, we will be preparing, processing, and storing features using SageMaker Feature Store. You can choose to run this notebook by itself or in sequence with the other notebooks listed below. Please see the [README.md](README.md) for more information about this use case implemented by this series of notebooks. \n",
+    "This notebook is the second part of a series of notebooks that will demonstrate how to prepare, train, and deploy a model that detects fradulent auto claims. In this notebook, we will be preparing, processing, and storing features using SageMaker Feature Store. You can choose to run this notebook by itself or in sequence with the other notebooks listed below. Please see the [README.md](README.md) for more information about this use case implemented by this series of notebooks. \n",
     "\n",
     "\n",
     "1. [Fraud Detection for Automobile Claims: Data Exploration](./0-AutoClaimFraudDetection.ipynb)\n",