Engineer-Data-in-Google-Cloud-Challenge-Lab
Engineer Data in Google Cloud: Challenge Lab
GSP327
CREATE OR REPLACE TABLE
taxirides.taxi_training_data AS
SELECT
(tolls_amount + fare_amount) AS fare_amount,
pickup_datetime,
pickup_longitude AS pickuplon,
pickup_latitude AS pickuplat,
dropoff_longitude AS dropofflon,
dropoff_latitude AS dropofflat,
passenger_count AS passengers,
FROM
taxirides.historical_taxi_rides_raw
WHERE
RAND() < 0.001
AND trip_distance > 0
AND fare_amount >= 2.5
AND pickup_longitude > -78
AND pickup_longitude < -70
AND dropoff_longitude > -78
AND dropoff_longitude < -70
AND pickup_latitude > 37
AND pickup_latitude < 45
AND dropoff_latitude > 37
AND dropoff_latitude < 45
AND passenger_count > 0
###############################################################################################################################3
CREATE OR REPLACE MODEL taxirides.fare_model
TRANSFORM(
* EXCEPT(pickup_datetime)
, ST_Distance(ST_GeogPoint(pickuplon, pickuplat),
ST_GeogPoint(dropofflon, dropofflat)) AS euclidean
, CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek
, CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday
)
OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg')
AS
SELECT * FROM taxirides.taxi_training_data
####################################################################################################################################
CREATE OR REPLACE TABLE taxirides.2015_fare_amount_predictions
AS
SELECT * FROM ML.PREDICT(MODEL taxirides.fare_model,(
SELECT * FROM taxirides.report_prediction_data)
)
very good content
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