Calculate trips taken by Yellow taxi in each month of 2015
Calculate average speed of Yellow taxi trips in 2015
Test whether fields are good inputs to your fare forecasting model
Create a BigQuery dataset to store models
Create a taxifare model
Evaluate classification model performance
Predict taxi fare amount
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 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. You will then 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 with it.
In this lab, you will 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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- 获取对“Google Cloud Console”的临时访问权限。
- 200 多项实验，从入门级实验到高级实验，应有尽有。