• 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 BigQuery ML
• Evaluate the performance of your machine learning model.
More Details:
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GCP-BigQueryML-ForecastingModel
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 BigQuery ML. Evaluate the performance of your machine learning model.
GCP-BigQueryML-ForecastingModel
• 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 BigQuery ML
• Evaluate the performance of your machine learning model.
Explore NYC taxi cab data
How many trips did Yellow Cab taxis take each month in 2015?
#standardSQL
SELECT
TIMESTAMP_TRUNC(pickup_datetime
MONTH) month
COUNT(*) trips
FROM
bigquery-public-data.new_york.tlc_yellow_trips_2015
GROUP BY
1
ORDER BY
1
Then click Run.
You should receive the following result:
As we see, every month in 2015 had over 10 million NYC taxi trips—no small amount!
Replace the previous query with AvgSpeed.sql
You should receive the following result:
During the day, the average speed is around 11-12 MPH; but at 5:00 AM the average speed almost doubles to 21 MPH. Intuitively this makes sense since there is likely less traffic…
PS: Sorry, couldn't repost here since there are some incompatible MD codes. Thank you!
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