Learn the key steps to leverage Amazon Redshift Machine Learning (Amazon Redshift ML) to build a customer churn prediction model and run predictions directly within your Redshift data warehouse. Improve your ML workflow and get accurate churn predictions.
Table of Contents
Question
A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.
A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.
Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.)
A. Define the feature variables and target variable for the churn prediction model.
B. Use the SOL EXPLAIN_MODEL function to run predictions.
C. Write a CREATE MODEL SQL statement to create a model.
D. Use Amazon Redshift Spectrum to train the model.
E. Manually export the training data to Amazon S3.
F. Use the SQL prediction function to run predictions.
Answer
A. Define the feature variables and target variable for the churn prediction model.
C. Write a CREATE MODEL SQL statement to create a model.
F. Use the SQL prediction function to run predictions.
Explanation
To use Amazon Redshift Machine Learning (Amazon Redshift ML) for building a customer churn prediction model and running predictions directly within the Redshift data warehouse, the company should take the following steps:
A. Define the feature variables and target variable for the churn prediction model. This involves identifying the relevant data attributes that can help predict customer churn, such as customer demographics, purchasing history, engagement metrics, etc. The target variable would be a binary indicator of whether a customer churned or not.
C. Write a CREATE MODEL SQL statement to create a model. With Amazon Redshift ML, you can use a simple SQL query to create and train a model. The CREATE MODEL statement specifies the type of model (e.g. logistic regression for binary classification), the training data, and the target and feature columns.
F. Use the SQL prediction function to run predictions. Once the model is trained, you can use the SQL prediction function to generate churn predictions for new data points directly within the Redshift data warehouse. The prediction function takes the trained model and input data as arguments and returns the predicted churn probability.
The other options are not necessary or applicable:
B. The EXPLAIN_MODEL function is used to get insights into how the model works, but not for running predictions.
D. Amazon Redshift Spectrum is used for querying data in Amazon S3, but not for training models.
E. With Amazon Redshift ML, there is no need to manually export training data to S3, as the model training happens directly within Redshift on the data stored there.
By following steps A, C and F, the company can leverage Amazon Redshift ML to build a churn prediction model and generate predictions in a streamlined manner, without moving data out of the data warehouse. This allows seamless integration of ML into the data workflow.
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