Create ML Models with BigQuery ML: Challenge Lab avis

Жандос О. · Examiné il y a plus d'un an

Octavia R. · Examiné il y a plus d'un an

Task 3 doesn't work

Pierpaolo S. · Examiné il y a plus d'un an

Md I. · Examiné il y a plus d'un an

Ozana I. · Examiné il y a plus d'un an

I finished Task 3 by creating and evaluating the new model improved_customer_classification_model with the required additional features, but it would never mark it as complete. Here's the query I used to create the model: CREATE OR REPLACE MODEL `ecommerce.improved_customer_classification_model` OPTIONS ( model_type='logistic_reg', labels = ['will_buy_on_return_visit'] ) AS #standardSQL SELECT * EXCEPT(fullVisitorId) FROM # features (SELECT fullVisitorId, IFNULL(totals.bounces, 0) AS bounces, IFNULL(totals.timeOnSite, 0) AS time_on_site, IFNULL(totals.pageviews, 0) AS pageviews, trafficSource.source, trafficSource.medium, channelGrouping, # mobile or desktop device.deviceCategory, # geographic IFNULL(geoNetwork.country, "") AS country FROM `data-to-insights.ecommerce.web_analytics` WHERE totals.newVisits = 1 AND date BETWEEN '20160801' AND '20170430') # train on first 9 months JOIN (SELECT fullvisitorid, IF(COUNTIF(totals.transactions > 0 AND totals.newVisits IS NULL) > 0, 1, 0) AS will_buy_on_return_visit FROM `data-to-insights.ecommerce.web_analytics` GROUP BY fullvisitorid) USING (fullVisitorId) ; And here's how I evaluated it: SELECT roc_auc, CASE WHEN roc_auc > .9 THEN 'good' WHEN roc_auc > .8 THEN 'fair' WHEN roc_auc > .7 THEN 'decent' WHEN roc_auc > .6 THEN 'not great' ELSE 'poor' END AS model_quality FROM ML.EVALUATE(MODEL ecommerce.improved_customer_classification_model, ( #standardSQL SELECT * EXCEPT(fullVisitorId) FROM # features (SELECT fullVisitorId, IFNULL(totals.bounces, 0) AS bounces, IFNULL(totals.timeOnSite, 0) AS time_on_site, IFNULL(totals.pageviews, 0) AS pageviews, trafficSource.source, trafficSource.medium, channelGrouping, # mobile or desktop device.deviceCategory, # geographic IFNULL(geoNetwork.country, "") AS country FROM `data-to-insights.ecommerce.web_analytics` WHERE totals.newVisits = 1 AND date BETWEEN '20160801' AND '20170430') # train on first 9 months JOIN (SELECT fullvisitorid, IF(COUNTIF(totals.transactions > 0 AND totals.newVisits IS NULL) > 0, 1, 0) AS will_buy_on_return_visit FROM `data-to-insights.ecommerce.web_analytics` GROUP BY fullvisitorid) USING (fullVisitorId) ));

Adam W. · Examiné il y a plus d'un an

Kevin C. · Examiné il y a plus d'un an

Juan Carlos B. · Examiné il y a plus d'un an

Manish S. · Examiné il y a plus d'un an

Jesús Arnulfo C. · Examiné il y a plus d'un an

Ilyas N. · Examiné il y a plus d'un an

William B. · Examiné il y a plus d'un an

Shannon M. · Examiné il y a plus d'un an

Shannon M. · Examiné il y a plus d'un an

Shannon M. · Examiné il y a plus d'un an

Andrew B. · Examiné il y a plus d'un an

Brenda L. · Examiné il y a plus d'un an

Dariga S. · Examiné il y a plus d'un an

Good exercise which is a full recap for the ML Models with BigQuery ML labs serie. You need to have good SQL knowledge and remember previous exercises.

Thierry A. · Examiné il y a plus d'un an

nartai s. · Examiné il y a plus d'un an

Nurken S. · Examiné il y a plus d'un an

Nurken S. · Examiné il y a plus d'un an

Task 2 is impossible to solve. First, it's said to use "logistic_regression" then just after "use linear regression". They say to store the model in "bqml_dataset", but in Task 1 we created a dataset "bq_dataset". No way to find what you really expected to pass the test, what to trust and what to do. It has to be fixed!

Loïc B. · Examiné il y a plus d'un an

Meghana M. · Examiné il y a plus d'un an

Aleksandar M. · Examiné il y a plus d'un an