Predict Taxi Fare with a BigQuery ML Forecasting Model
BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage, or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
BigQuery Machine Learning (BQML, product in beta) is a new feature in BigQuery where data analysts can create, train, evaluate, and predict with machine learning models with minimal coding.
In this lab, you will explore millions of New York City yellow taxi cab trips available in a BigQuery Public Dataset. Then you will create a machine learning model inside of BigQuery to predict the fare of the cab ride given your model inputs. Lastly, you will evaluate the performance of your model and make predictions.
In this lab, you learn to perform the following tasks:
Use BigQuery to find public datasets
Query and explore the public taxi cab dataset
Create a training and evaluation dataset to be used for batch prediction
Create a forecasting (linear regression) model in BQML
Evaluate the performance of your machine learning model
What you'll need
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