Creating and Evaluating ML Models.sql

# Creating Logistic Regression ML Model using two features "bounces" & "time_on_site"

CREATE OR REPLACE MODEL `ecommerce.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
  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)
;

# Evaluating ML Model using ROC Curves

SELECT
  roc_auc,
  CASE
    WHEN roc_auc > .9 THEN 'good'
    WHEN roc_auc > .8 THEN 'fair'
    WHEN roc_auc > .7 THEN 'not great'
  ELSE 'poor' END AS model_quality
FROM
  ML.EVALUATE(MODEL ecommerce.classification_model,  (

SELECT
  * EXCEPT(fullVisitorId)
FROM

  # features
  (SELECT
    fullVisitorId,
    IFNULL(totals.bounces, 0) AS bounces,
    IFNULL(totals.timeOnSite, 0) AS time_on_site
  FROM
    `data-to-insights.ecommerce.web_analytics`
  WHERE
    totals.newVisits = 1
    AND date BETWEEN '20170501' AND '20170630') # eval on 2 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)

));

# Improving Logistic Regression ML Model using more relevant features 

CREATE OR REPLACE MODEL `ecommerce.classification_model_2`
OPTIONS
  (model_type='logistic_reg', labels = ['will_buy_on_return_visit']) AS

WITH all_visitor_stats AS (
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
)

# add in new features
SELECT * EXCEPT(unique_session_id) FROM (

  SELECT
      CONCAT(fullvisitorid, CAST(visitId AS STRING)) AS unique_session_id,

      # labels
      will_buy_on_return_visit,

      MAX(CAST(h.eCommerceAction.action_type AS INT64)) AS latest_ecommerce_progress,

      # behavior on the site
      IFNULL(totals.bounces, 0) AS bounces,
      IFNULL(totals.timeOnSite, 0) AS time_on_site,
      totals.pageviews,

      # where the visitor came from
      trafficSource.source,
      trafficSource.medium,
      channelGrouping,

      # mobile or desktop
      device.deviceCategory,

      # geographic
      IFNULL(geoNetwork.country, "") AS country

  FROM `data-to-insights.ecommerce.web_analytics`,
     UNNEST(hits) AS h

    JOIN all_visitor_stats USING(fullvisitorid)

  WHERE 1=1
    # only predict for new visits
    AND totals.newVisits = 1
    AND date BETWEEN '20160801' AND '20170430' # train 9 months

  GROUP BY
  unique_session_id,
  will_buy_on_return_visit,
  bounces,
  time_on_site,
  totals.pageviews,
  trafficSource.source,
  trafficSource.medium,
  channelGrouping,
  device.deviceCategory,
  country
);

# Evaluating the Model

SELECT
  roc_auc,
  CASE
    WHEN roc_auc > .9 THEN 'good'
    WHEN roc_auc > .8 THEN 'fair'
    WHEN roc_auc > .7 THEN 'not great'
  ELSE 'poor' END AS model_quality
FROM
  ML.EVALUATE(MODEL ecommerce.classification_model_2,  (

WITH all_visitor_stats AS (
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
)

# add in new features
SELECT * EXCEPT(unique_session_id) FROM (

  SELECT
      CONCAT(fullvisitorid, CAST(visitId AS STRING)) AS unique_session_id,

      # labels
      will_buy_on_return_visit,

      MAX(CAST(h.eCommerceAction.action_type AS INT64)) AS latest_ecommerce_progress,

      # behavior on the site
      IFNULL(totals.bounces, 0) AS bounces,
      IFNULL(totals.timeOnSite, 0) AS time_on_site,
      totals.pageviews,

      # where the visitor came from
      trafficSource.source,
      trafficSource.medium,
      channelGrouping,

      # mobile or desktop
      device.deviceCategory,

      # geographic
      IFNULL(geoNetwork.country, "") AS country

  FROM `data-to-insights.ecommerce.web_analytics`,
     UNNEST(hits) AS h

    JOIN all_visitor_stats USING(fullvisitorid)

  WHERE 1=1
    # only predict for new visits
    AND totals.newVisits = 1
    AND date BETWEEN '20170501' AND '20170630' # eval 2 months

  GROUP BY
  unique_session_id,
  will_buy_on_return_visit,
  bounces,
  time_on_site,
  totals.pageviews,
  trafficSource.source,
  trafficSource.medium,
  channelGrouping,
  device.deviceCategory,
  country
)
));