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Predict Taxi Fare with a BigQuery ML Forecasting Model

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Checkpoints

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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

1 个小时 7 个积分

GSP246

Google Cloud Self-Paced Labs

Overview

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.

Objectives

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。
  • 内容短小精悍,便于您按照自己的节奏进行学习。
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