In our dataset, a case was a certain date during the skiing season with weather and snow information, overnight stays of visitors in the area, and exchange rate information from the most common countries of the foreign visitors. The information for each case (record) must be stored in a separate row. It could also be prepared as a view if the data doesn’t need to be moved and duplicated.ĭata for mining must exist within a single table or view. □ The data needs to be well-labelled, annotated and cleansed (resolve keys, aggregate numbers to the granularity needed).ĭata doesn’t necessarily need to be persisted into a table to work for a data mining model.□ All data - that should be analyzed - needs to be in one single table or view.The following things had to be considered when preparing the data: We needed to get from multiple tables with selected, specific data, to one table with all the necessary, aggregated, relevant data. To be able to build a data mining model, we needed to restructure and prepare the data to get one single table or view. To import the data (received as Excel and CSV Files from the customer) the import functionality of SQL Developer is very useful. The query includes information about the predictors that have the greatest influence on the prediction.Oracle Cloud Dashboard with Quick Options - Creating a data warehouse The query returns the 3 customers whose predicted age is most different from the actual. In this example, dynamic regression is used to predict the age of customers who are likely to use an affinity card. The cost matrix specifies that the misclassification of 1 is 8 times more costly than the misclassification of 0.ĪCTUAL_TARGET_VALUE PREDICTED_TARGET_VALUE COST The cost matrix associated with the model dt_sh_clas_sample is stored in the table dt_sh_sample_costs. USING cust_marital_status, education, household_size) = 1 WHERE PREDICTION(dt_sh_clas_sample COST MODEL SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age The PREDICTION function takes into account the cost matrix associated with the model and uses marital status, education, and household size as predictors. In this example, the model dt_sh_clas_sample predicts the gender and age of customers who are most likely to use an affinity card (target = 1). The syntax of the PREDICTION function can use an optional GROUPING hint when scoring a partitioned model. (See " analytic_clause::=".)įor Regression, specify FOR expr, where expr is an expression that identifies a target column that has a numeric data type.įor Classification, specify FOR expr, where expr is an expression that identifies a target column that has a character data type.įor Anomaly Detection, specify the keywords OF ANOMALY. The mining_analytic_clause supports a query_partition_clause and an order_by_clause. The analytic syntax uses mining_analytic_clause, which specifies if the data should be partitioned for multiple model builds. Supply the name of a model that performs Classification, Regression, or Anomaly Detection.Īnalytic Syntax: Use the analytic syntax to score the data without a pre-defined model. Syntax: Use this syntax to score the data with a pre-defined model. PREDICTION can score data in one of two ways: It can apply a mining model object to the data, or it can dynamically score the data by executing an analytic clause that builds and applies one or more transient mining models.
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