KNIME – Quick Guide KNIME – Introduction Developing Machine Learning models is always considered very challenging due to its cryptic nature. Generally, to develop machine learning applications, you must be a good developer with an expertise in command-driven development. The introduction of KNIME has brought the development of Machine Learning models in the purview of a common man. KNIME provides a graphical interface (a user friendly GUI) for the entire development. In KNIME, you simply have to define the workflow between the various predefined nodes provided in its repository. KNIME provides several predefined components called nodes for various tasks such as reading data, applying various ML algorithms, and visualizing data in various formats. Thus, for working with KNIME, no programming knowledge is required. Isn’t this exciting? The upcoming chapters of this tutorial will teach you how to master the data analytics using several well-tested ML algorithms. KNIME – Installation KNIME Analytics Platform is available for Windows, Linux and MacOS. In this chapter, let us look into the steps for installing the platform on the Mac. If you use Windows or Linux, just follow the installation instructions given on the KNIME download page. The binary installation for all three platforms is available at . Mac Installation Download the binary installation from the KNIME official site. Double click on the downloaded dmg file to start the installation. When the installation completes, just drag the KNIME icon to the Applications folder as seen here − KNIME – First Run Double-click the KNIME icon to start the KNIME Analytics Platform. Initially, you will be asked to setup a workspace folder for saving your work. Your screen will look like the following − You may set the selected folder as default and the next time you launch KNIME, it will not show up this dialog again. After a while, the KNIME platform will start on your desktop. This is the workbench where you would carry your analytics work. Let us now look at the various portions of the workbench. KNIME – Workbench When KNIME starts, you will see the following screen − As has been marked in the screenshot, the workbench consists of several views. The views which are of immediate use to us are marked in the screenshot and listed below − Workspace Outline Nodes Repository KNIME Explorer Console Description As we move ahead in this chapter, let us learn these views each in detail. Workspace View The most important view for us is the Workspace view. This is where you would create your machine learning model. The workspace view is highlighted in the screenshot below − The screenshot shows an opened workspace. You will soon learn how to open an existing workspace. Each workspace contains one or more nodes. You will learn the significance of these nodes later in the tutorial. The nodes are connected using arrows. Generally, the program flow is defined from left to right, though this is not required. You may freely move each node anywhere in the workspace. The connecting lines between the two would move appropriately to maintain the connection between the nodes. You may add/remove connections between nodes at any time. For each node a small description may be optionally added. Outline View The workspace view may not be able to show you the entire workflow at a time. That is the reason, the outline view is provided. The outline view shows a miniature view of the entire workspace. There is a zoom window inside this view that you can slide to see the different portions of the workflow in the Workspace view. Node Repository This is the next important view in the workbench. The Node repository lists the various nodes available for your analytics. The entire repository is nicely categorized based on the node functions. You will find categories such as − IO Views Analytics Under each category you would find several options. Just expand each category view to see what you have there. Under the IO category, you will find nodes to read your data in various file formats, such as ARFF, CSV, PMML, XLS, etc. Depending on your input source data format, you will select the appropriate node for reading your dataset. By this time, probably you have understood the purpose of a node. A node defines a certain kind of functionality that you can visually include in your workflow. The Analytics node defines the various machine learning algorithms, such as Bayes, Clustering, Decision Tree, Ensemble Learning, and so on. The implementation of these various ML algorithms is provided in these nodes. To apply any algorithm in your analytics, simply pick up the desired node from the repository and add it to your workspace. Connect the output of the Data reader node to the input of this ML node and your workflow is created. We suggest you to explore the various nodes available in the repository. KNIME Explorer The next important view in the workbench is the Explorer view as shown in the screenshot below − The first two categories list the workspaces defined on the KNIME server. The third option LOCAL is used for storing all the workspaces that you create on your local machine. Try expanding these tabs to see the various predefined workspaces. Especially, expand EXAMPLES tab. KNIME provides several examples to get you started with the platform. In the next chapter, you will be using one of these examples to get yourself acquainted with the platform. Console View As the name indicates, the Console view provides a view of the various console messages while executing your workflow. The Console view is useful in diagnosing the workflow and examining the analytics results. Description View The last important view that is of immediate relevance to us is the Description view. This view provides a description of a selected item in the workspace. A typical view is shown in the screenshot below − The above view shows the description of a File Reader node. When you select the File Reader node in