Predicting the probability of conversion from features using BigQuery ML.

To predict the probability of conversion using BigQuery ML, you would need to adjust your problem formulation and use logistic regression instead of linear regression. Here's how you can modify the previous example:

1. Ensure your table schema includes a binary conversion outcome (1 for converted, 0 for not converted):

   • season (STRING)

   • num_clicks (INTEGER)

   • feature_1 (FLOAT64)

   • feature_2 (FLOAT64)

   • converted (INTEGER)

2. Create a logistic regression model using BigQuery ML:

CREATE OR REPLACE MODEL `your_project_id.your_dataset.conversion_probability_model`
OPTIONS(model_type="logistic_reg") AS
SELECT
  season,
  num_clicks,
  feature_1,
  feature_2,
  converted
FROM
  `your_project_id.your_dataset.your_table`;

1. Evaluate the model's performance using the ML.EVALUATE function:

SELECT
  *
FROM
  ML.EVALUATE(MODEL `your_project_id.your_dataset.conversion_probability_model`,
    (
    SELECT
      season,
      num_clicks,
      feature_1,
      feature_2,
      converted
    FROM
      `your_project_id.your_dataset.your_table`));

1. Use the ML.PREDICT function to predict the probability of conversion:

SELECT
  season,
  num_clicks,
  feature_1,
  feature_2,
  predicted_converted_probs
FROM
  ML.PREDICT(MODEL `your_project_id.your_dataset.conversion_probability_model`,
    (
    SELECT
      season,
      num_clicks,
      feature_1,
      feature_2
    FROM
      `your_project_id.your_dataset.your_table`));

This will return the predicted probability of conversion for each row in your table, along with the input features. Remember to replace your_project_id, your_dataset, and your_table with the appropriate values from your Google BigQuery environment.