Step 5: Use the ML Model to Generate Predictions

Amazon Machine Learning (Amazon ML) can generate two types of predictions—batch and real-time.

A real-time prediction is a prediction for a single observation that Amazon ML generates on demand. Real-time predictions are ideal for mobile apps, websites, and other applications that need to use results interactively.

A batch prediction is a set of predictions for a group of observations. Amazon ML processes the records in a batch prediction together, so processing can take some time. Use batch predictions for applications that require predictions for set of observations or predictions that don't use results interactively.

For this tutorial, you will generate a real-time prediction that predicts whether one potential customer will subscribe to the new product. You will also generate predictions for a large batch of potential customers. For the batch prediction, you will use the banking-batch.csv file that you uploaded in Step 1: Prepare Your Data.

Let's start with a real-time prediction.

Note

For applications that require real-time predictions, you must create a real-time endpoint for the ML model. You accrue charges while a real-time endpoint is available. Before you commit to using real-time predictions and begin incurring the cost associated with them, you can try using the real-time prediction feature in your web browser, without creating a real-time endpoint. That's what we'll do for this tutorial.

To try a real-time prediction

1. In the ML model report navigation pane, choose Try real-time predictions.

   

2. Choose Paste a record.

   

3. In the Paste a record dialog box, paste the following observation:

   32,services,divorced,basic.9y,no,unknown,yes,cellular,dec,mon,110,1,11,0,nonexistent,-1.8,94.465,-36.1,0.883,5228.1

4. In the Paste a record dialog box, choose Submit to confirm that you want to generate a prediction for this observation. Amazon ML populates the values in the real-time prediction form.

   

   Note

   You can also populate the Value fields by typing in individual values. Regardless of the method you choose, you should provide an observation that wasn't used to train the model.

5. At the bottom of the page, choose Create prediction.

   The prediction appears in the Prediction results pane on the right. This prediction has a Predicted label of 0, which means that this potential customer is unlikely to respond to the campaign. A Predicted label of 1 would mean that the customer is likely to respond to the campaign.

   

Now, create a batch prediction. You will provide Amazon ML with the name of the ML model you are using; the Amazon Simple Storage Service (Amazon S3) location of the input data for which you want to generate predictions (Amazon ML will create a batch prediction datasource from this data); and the Amazon S3 location for storing the results.

To create a batch prediction

1. Choose Amazon Machine Learning, and then choose Batch Predictions.

   

2. Choose Create new batch prediction.

3. On the ML model for batch predictions page, choose ML model: Banking Data 1.

   Amazon ML displays the ML model name, ID, creation time, and the associated datasource ID.

4. Choose Continue.

5. To generate predictions, you need to provide Amazon ML the data that you need predictions for. This is called the input data. First, put the input data into a datasource so that Amazon ML can access it.

   For Locate the input data, choose My data is in S3, and I need to create a datasource.

   

6. For Datasource name, type Banking Data 2.

7. For S3 Location, type the full location of the banking-batch.csv file: your-bucket/banking-batch.csv.

8. For Does the first line in your CSV contain the column names?, choose Yes.

9. Choose Verify.

   Amazon ML validates the location of your data.

10. Choose Continue.

11. For S3 destination, type the name of the Amazon S3 location where you uploaded the files in Step 1: Prepare Your Data. Amazon ML uploads the prediction results there.

12. For Batch prediction name, accept the default, Batch prediction: ML model: Banking Data 1. Amazon ML chooses the default name based on the model it will use to create predictions. In this tutorial, the model and the predictions are named after the training datasource, Banking Data 1.

13. Choose Review.

14. In the S3 permissions dialog box, choose Yes.

    

15. On the Review page, choose Finish.

    The batch prediction request is sent to Amazon ML and entered into a queue. The time it takes Amazon ML to process a batch prediction depends on the size of your datasource and the complexity of your ML model. While Amazon ML processes the request, it reports a status of In Progress. After the batch prediction has completed, the request's status changes to Completed. Now, you can view the results.

To view the predictions

1. Choose Amazon Machine Learning, and then choose Batch Predictions.

   

2. In the list of predictions, choose Batch prediction: ML model: Banking Data 1. The Batch prediction info page appears.

   

3. To view the results of the batch prediction, go to the Amazon S3 console at https://console.aws.amazon.com/s3/ and navigate to the Amazon S3 location referenced in the Output S3 URL field. From there, navigate to the results folder, which will have a name similar to s3://aml-data/batch-prediction/result.

   

   The prediction is stored in a compressed .gzip file with the .gz extension.

4. Download the prediction file to your desktop, uncompress it, and open it.

   

   The file has two columns, bestAnswer and score, and a row for each observation in your datasource. The results in the bestAnswer column are based on the score threshold of 0.77 that you set in Step 4: Review the ML Model's Predictive Performance and Set a Score Threshold. A score greater than 0.77 results in a bestAnswer of 1, which is a positive response or prediction, and a score less than 0.77 results in a bestAnswer of 0, which is a negative response or prediction.

   The following examples show positive and negative predictions based on the score threshold of 0.77.

Positive prediction:

In this example, the value for bestAnswer is 1, and the value of score is 0.8228876. The value for bestAnswer is 1 because the score is greater than the score threshold of 0.77. A bestAnswer of 1 indicates that the customer is likely to purchase your product, and is, therefore, considered a positive prediction.

Negative prediction:

In this example, the value of bestAnswer is 0 because the score value is 0.7695356, which is less than the score threshold of 0.77. The bestAnswer of 0 indicates that the customer is unlikely to purchase your product, and is, therefore, considered a negative prediction.

Each row of the batch result corresponds to a row in your batch input (an observation in your datasource).

After analyzing the predictions, you can execute your targeted marketing campaign; for example, by sending fliers to everyone with a predicted score of 1.

Now that you have created, reviewed, and used your model, clean up the data and AWS resources you created to avoid incurring unnecessary charges and to keep your workspace uncluttered.