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In BIDS, in the Data Mining Designer, click the Mining Accuracy Chart tab. In the Input Selection window, make sure that all three mining models are selected in the Select Predictable Mining Model Columns to Show in the Lift Chart table and that the Synchronize Prediction Columns and Values check box is checked. Also make sure that in the Select Data Set to Be Used for Accuracy Chart option group, the Use Mining Model Test Cases option is selected. Leave the Filter expression box empty. In the Predict Value column of the Select Predictable Mining Model Columns to Show in the Lift Chart table, select 1 in the drop-down list of any row. We re interested in showing the performance of the models when predicting positive outcome when predicting buyers only. Click the Lift Chart tab. Examine the Lift Chart. Don t exit BIDS.

The file name or names of the attached files, returned as a list for example, {"Literary Traumadocx","Sheep Design in the Rockiesjpg"} The MIME type of the attachment for example, text/plain for a text file, image/jpeg for a JPEG file, application/msword for a Microsoft Word document, application/pdf for a PDF file, or application/zip for a zip file This property returns a list for example, {"application/pdf", "application/zip", "text/plain"} for three attachments The approximate size of the attached file, measured in bytes, as a list for example, {228777, 896834, 482525} for three attachments Because sending a file as an attachment adds overhead, the attachment s file size is larger than the size of the file you end up removing from the message true if Mail has downloaded the file from the server; false if it has not Again, you get a list for example, {false,false} for a brace of attachments that are still on the server A text ID that uniquely identifies the attached file within the message (rather than globally) for example, 1 or 12 Once more, you get a list for example, {"1"} for a single attachment

Finally, it s time to select probable buyers from the ProspectiveBuyer table. You can do this task with a DMX prediction join query. Detailed description of the DMX language is out of scope for this book. Fortunately, you can get a jump start with the Prediction Query builder in BIDS. A similar builder is included in the RS Report Designer when you re authoring a report based on a data mining model, and in SSIS Package Designer in the Data Mining Query task and Data Mining Query transformation. In order to create a DMX prediction query in BIDS, follow these steps:

time The interviewer asked me, What do you call those bands I actually didn t have a name for them, so I said, My trading bands Then I quickly blurted out, Bollinger Bands And that s how they got named

In the Data Mining Designer, select the Mining Model Prediction tab. In the Select Model window, select the best-performing model. In this case, this is the Decision Trees model, which should be selected by default, as it was the first model created. In the Select Input Table(s) window, click on the Select Case table button. Select the ProspectiveBuyer table. Click OK. Check the join between model and table; it s done automatically based on column names. You can change the join manually, though following a consistent naming convention is a good practice. In the bottom pane, define the column list of your DMX Select statement. In the Source and Field columns, select the following attributes: ProspectiveBuyerKey, FirstName, and LastName, from the ProspectiveBuyer table, and Bike Buyer from the TM_DT mining model. Your selections in the Prediction Query builder should look like the selections in figure 5.

This is fine if you know that the message has an attachment If not, you need to test The following code shows one way of finding messages with attachments using a repeat loop to walk through the messages in a mailbox one by one, checking each to see if the name property of the first item of the mail attachment is not equal to a blank string (" "), and then saving it with a similar technique to that described previously