edit aws_marketplace/using_model_packages/auto_insurance/automating_auto_insurance_claim_processing.ipynb

Author: EC2 Default User

aws_marketplace/using_model_packages/auto_insurance/automating_auto_insurance_claim_processing.ipynb

@@ -4,12 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Goal: Automate Auto Insurance Claim Processing Using Pre-trained Models \n",
+    "# Goal: Automate Auto Insurance Claim Processing Using Pre-trained Models \n",
     "Auto insurance claim process requires extracting metadata from images and performing validations to ensure that the claim is not fraudulent. This sample notebook shows how third party pre-trained machine learning models can be used to extract such metadata from images.\n",
     "\n",
     "This notebook uses [Vehicle Damage Inspection](https://aws.amazon.com/marketplace/pp/Persistent-Systems-Vehicle-Damage-Inspection/prodview-xhj66rbazm6oe) model to identify the type of damage and [Deep Vision vehicle recognition](https://aws.amazon.com/marketplace/pp/prodview-a7wgrolhu54ts?qid=1558356141251&sr=0-4&ref_=srh_res_product_title) to identify the make, model, year, and bounding box of the car. This notebook also shows how to use the bounding box to extract license information from the using [Amazon Rekognition](https://aws.amazon.com/rekognition/).\n",
     "\n",
-    "### Pre-requisites:\n",
+    "## Pre-requisites\n",
     "This sample notebook requires subscription to following pre-trained machine learning model packages from AWS Marketplace:\n",
     "\n",
     "1. [Vehicle Damage Inspection](https://aws.amazon.com/marketplace/pp/Persistent-Systems-Vehicle-Damage-Inspection/prodview-xhj66rbazm6oe)\n",
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Set up environment and view a sample image\n",
+    "## Set up environment and view a sample image\n",
     "\n",
     "In this section, we will import necessary libraries and define variables such as an S3 bucket, an IAM role, and SageMaker session to be used."
    ]
@@ -311,7 +311,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3. Extract labels from the picture (optional)\n",
+    "## Step 3: Extract labels from the picture (optional)\n",
     "\n",
     "Let us use the car image extracted from the original image for extracting license information using [Amazon Rekognition](https://aws.amazon.com/rekognition/).\n",
     "\n",
@@ -392,7 +392,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5. Cleanup "
+    "## 5: Cleanup "
    ]
   },
   {