Train and deploy a Hotel Recommender System using the public Expedia data from Kaggle competition https://www.kaggle.com/c/expedia-hotel-recommendations/overview

The goal is to predict which hotel type an Expedia customer will book!

• Python 3.6+ is required
• make create_environment, then conda activate personalize_hotels and, finally, make requirements in order to create a virtualenv
• AWS CLI is installed and ~/.aws/credentials is configured with the right profile
• Download data (expedia-hotel-recommendations.zip) from https://www.kaggle.com/c/expedia-hotel-recommendations/data and unzip the downloaded file in the root directory of the project.
• Add (and execute) the following to your Jupyter notebook to create a Personalize Role:

  import boto3 
  
  session = boto3.Session(profile_name='<your-aws-profile>')
  iam = session.client("iam")
  
  role_name = "PersonalizeRole"
  assume_role_policy_document = {
      "Version": "2012-10-17",
      "Statement": [
          {
            "Effect": "Allow",
            "Principal": {
              "Service": "personalize.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
          }
      ]
  }
  
  create_role_response = iam.create_role(
      RoleName = role_name,
      AssumeRolePolicyDocument = json.dumps(assume_role_policy_document)
  )
  
  # AmazonPersonalizeFullAccess provides access to any S3 bucket with a name that includes "personalize" or "Personalize" 
  # if you would like to use a bucket with a different name, please consider creating and attaching a new policy
  # that provides read access to your bucket or attaching the AmazonS3ReadOnlyAccess policy to the role
  policy_arn = "arn:aws:iam::aws:policy/service-role/AmazonPersonalizeFullAccess"
  iam.attach_role_policy(
      RoleName = role_name,
      PolicyArn = policy_arn
  )
  
  time.sleep(60) # wait for a minute to allow IAM role policy attachment to propagate
  
  role_arn = create_role_response["Role"]["Arn"]
  print(role_arn)