IBM SPSS Statistics evolved from an original product that was released in 1968. That product was called āStatistical Package for Social Sciences,ā or āSPSS.ā IBM SPSS Statistics is a statistical and machine learning software application and is widely used in academia, government agencies, and large enterprises. Itās used to build predictive models, perform statistical analysis of data, and conduct other analytic tasks. It has a visual interface, which enables users to leverage statistical and data mining algorithms without programming, although the interface is very different from Modeler. As you can see, the main section of the screen looks very much like a spreadsheet; it displays data and allows manual editing. This particular small data set, called āEmployee Dataā, was created some time ago and does not represent real people. It is shipped with the product for use in demos and tutorials. At the bottom of the screen, we can see two tabs: Data View and Variable View. In the Variable View, we can see and edit the information about all variables, including names, labels, data types, and measurement levels. We can also specify labels for values of categorical variables, and missing values. At the top of the data window is a menu. Under File, if you select āImport Data,ā you will see a list of a wide variety of data formats that you can import. The product uses its own data file format with the extension ā.savā that saves all the information about the variables we just saw in Variable view. The menu enables importing from and exporting to many other formats. Under āData,ā youāll find an extensive menu of possible data operations. Note that Data Validation can be performed using user-defined rules that specify the expected behavior of variable values. For example, if the date and month are kept in separate columns, the date cannot exceed ā31,ā but for February, the date canāt exceed ā29.ā A special rule can therefore be created and applied during data validation. Additionally, you can enable some checks, such as percentage of missing values in a record or in the field. When you click the āTransformā menu item, youāll find a variety of available data transformations. Under āCompute Variableā¦ā you can write a formula for a new variable based on existing variables. You can use any of the many mathematical and statistical functions available in the product. You also have the option to use automatic data preparation, similar to Modeler. In the āAnalyzeā menu, you will see many types of statistical and machine learning analysis. Under āRegression,ā there are a variety of regression-related models. There are other kinds of regressions that appear separately on the Analyze menu, including General Linear Model, Generalized Linear Models, Mixed Models, and Loglinear. Now letās build a decision-tree model on the data. For this exercise weāll try to predict the "Employment category" field based on other fields. In the āAnalyzeā menu, select āClassifyā and then āTreeā. <Click> In the Decision Tree window, we can specify the dependent variable āEmployment Category,ā and use most other fields -- except id and bdate -- as predictors, or independent variables. Usually the ID variable should not be used as a predictor, because it will not help with new cases, and the birthdate does not seem to be a useful predictor in this example either. Weāll select āExhaustive CHAIDā as our Growing Method, although there are also three other options available. Data scientists often try many different models to see which one works best for their data. Here we are just looking at one example model in order to illustrate how the product works. Click the āValidationā button to open the Decision Tree Validation window. Here, we select āSplit-sample validationā to make sure we test the model on new data. Click āOKā in the Decision Tree window, to <Click> generate the output, including the tree diagram shown here. <Click> A Classification table is also displayed that shows how well the model works on training and test data. In this case, the accuracy is 91.2% on training data and only 85.6% on test data, which means the model does not generalize to new data very well. Itās possible that by using different models, we can get better results. Letās move to the next menu item. When you click āGraphs,ā youāll open a versatile Chart Builder, in addition to several other options. The Chart Builder enables us to choose a style from the gallery and to drag required fields onto the canvas, select colors, and choose from other options. Hereās an example after we drag the āPrevious Experience,ā āCurrent Salary,ā and Gender variables to the corresponding slots to define the axis and colors for the dots on the chart. The plot in the canvas is not based on real data, this example simply gives you an idea of what to expect. Here is the real plot obtained from the data that weāve been using. It shows different colored dots for gender, and regression lines that show the relationship of the current salary to previous experience for each gender. Throughout IBM SPSS Statistics, youāll see a āPasteā button. When you click the āPasteā button, instead of executing the task right away the application will open another window, called the Syntax editor. Here, you can see the code called āsyntaxā pasted for you. SPSS syntax is a special programming language. For example, here is the code for the decision tree we just built. Once we have the syntax, we can execute it, manually edit it, store it for later use, or send it to other users of IBM SPSS Statistics. Experienced SPSS users can write the code from scratch, while others might prefer to have it generated by the graphical interface. Remember, the option to paste syntax is available in throughout the program. If the syntax is generated by all the steps in a data analytics process -- opening the data set, applying any data transformations, building models -- and then saved as a syntax file with the extension ā.spsā, itās similar to saving a stream in IBM SPSS Modeler. However, one important difference is that it does not allow for an easy way of scoring new records with the model. Weāll talk about different ways to deploy models in the next section. Youāve learned how IBM SPSS Statistics helps data scientists to analyze their data using many statistical and machine learning techniques. Using a graphical user interface, we can create complicated analysis that can be saved in the form of syntax and reused later. Next, we will talk about predictive model deployment, an important part of the overall data science lifecycle.
