From the course: Data Science on Google Cloud Platform: Predictive Analytics
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Predictive analytics process - Google Cloud Tutorial
From the course: Data Science on Google Cloud Platform: Predictive Analytics
Predictive analytics process
- In this video, I will review the various steps in the predictive analytics process and illustrate how this process is achieved in Cloud ML. Predictive analytics starts with setting a goal for solving a problem. Usually, it involves predicting the behavior of an element, or outcome of an activity. First, data needs to be acquired from its sources. It then goes through processing, which might involve cleansing, transformations, enhancements, and aggregations. Then, a training data set is created on which the model is built. The model is tested against the test data set. Once the model is confirmed to be good, it is then used for predicting future behavior or outcomes. How does the predictive analytics process work in Cloud ML? The work of collecting data, processing and building the model are usually done offline. GCP components like Pub/Sub, BigQuery, Cloud DataFlow, and Cloud Storage may also be used for this process. Model building can also be done as Cloud ML jobs, but they can be…
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