Predict Taxi Fare with a BigQuery ML Forecasting Model
GSP246
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
- A Google Cloud Platform Project
- A Browser, such as Google Chrome or Mozilla Firefox.
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- Get temporary access to the Google Cloud Console.
- Over 200 labs from beginner to advanced levels.
- Bite-sized so you can learn at your own pace.
Score
—/100
Calculate trips taken by Yellow taxi in each month of 2015
/ 10
Calculate average speed of Yellow taxi trips in 2015
/ 10
Test whether fields are good inputs to your fare forecasting model
/ 20
Create a BigQuery dataset to store models
/ 10
Create a taxifare model
/ 20
Evaluate classification model performance
/ 10
Predict taxi fare amount
/ 20