STEP 1: Create a dataset
First you should connect a dataset in Analyzr. Pick one of the demo data files above. The following steps will assume you picked the Marketing Campaign dataset. Create a CSV dataset using these steps.
STEP 2: Create a model
Next, create a model associated with the dataset you just created. Pick the Propensity Model option when creating your model. You can come back later and try a different model type. Create a model using these steps.
STEP 3: Explore the data used by your model
Select the model you just created in the Models page then click Explore in the side navigation bar. You will need to load the Marketing Campaign CSV file. Click here if you want to learn more about loading CSV files in Analyzr. Once it is loaded you can click on the Explore Variables step and review the variables in your dataset. Once you are done click on the Next Steps step. You are now ready to start the Training phase.
STEP 4: Train your model
Click Train in the side navigation bar. In the Select Variables step you will need to select the variables you want to use with your model. Use the "Select all filtered and valid as independent variables" in the bulk action dropdown then click Apply. In the ID row double-click on the variable type and change it to RecordIndex. In the Response row double-click on the variable type and change it to Dependent. You should now have 1 index variable, 1 dependent variable, and 26 independent variables selected.
In the Select Algorithm step select Logistic regression classifier for now. You can come back later and pick a different algorithm. Since the positive outcomes we are measuring with the Response variable are relatively infrequent, we will use SMOTE pre-processing to focus training on these positive outcomes. Keep other settings unchanged.
In the Train Model step, click Start to start training your model. Click here to learn how data can be encoded prior to processing by the analytics engine.
Once training is complete you are ready to review results; click the Review Propensity Metrics step to do so.
You may end up with slightly different results due to random sampling of the data when splitting your dataset into a training set and a validation set. After reviewing results click on the Next Steps step to complete the Training phase.
STEP 5: Predict using your model
You are now ready to predict new passenger records using your model! To do so follow these steps.
STEP 6: Wrap-Up
Once you are done with this exercise, feel free to delete your model by going to the Models page, selecting your model, clicking on the ellipsis in the top right corner of the model card and selecting Delete Model. Once your model is deleted you will be able to also delete your dataset.