Predict Taxi Fare with a BigQuery ML Forecasting Model

11 RSVP'ed

About this event

Overview of the lab:

Organisations are generating incredible value from their data using Google Cloud's analytics platform. At the heart of the platform is Google Cloud BigQuery - a fully managed, No-Ops, low cost, petabyte scale OLAP database. With BigQuery you can query terabytes of data without having any infrastructure to manage, or needing a database administrator.

BigQuery Machine Learning is a new feature in BigQuery where data analysts can create, train, evaluate, and predict with machine learning models using standard SQL queries.

Searce and Google Cloud are hosting a Study Jam — a unique concept of coding live on a Google Cloud Platform sandbox with the industry experts where 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


Thursday, Dec 3
11:00 AM - 1:00 PM (GMT)


  • Franco Camba

    Franco Camba

    Google Cloud UKI

    Customer Engineer

  • Megha Desai

    Megha Desai

    Searce UKI

    Technical Lead - Machine Learning


  • Ricardo La Rosa

    Ricardo La Rosa


    GDG Organizer

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  • Swapnil Parashar

    Swapnil Parashar


    Software Engineering Manager

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