Introduction:
Knime ( Konstanz Information Miner ) is an open source data analytics platform that allows the users to create different visual workflows for data science. Workflows consist of nodes, which represent data processing or analysis steps, connected in a sequence to form branching. It supports integration with various programming languages and libraries, allowing users to extend its functionality. It provides tools for advanced analytics, machine learning, big data integration and data visualization. All in all, it combines various tools, making it a perfect tool for data analysis, machine learning and automation without the extensive coding knowledge.
Installation & Setup:
Step 1: Download KNIME:
Visit the official website KNIME and select the Download option at top-right corner of the interface. Tick both the checkboxes, then click on the download button to proceed.
Click the installation option that fits your operating system ( Windows, Linux or MacOS ).
Step 2: Install KNIME:
Follow all the onscreen instructions to complete the installation. Ensure a Java Runtime Environment (JRE) or Java Development Kit (JDK) must be available in order to run KNIME.
Step 3: Launch KNIME:
Open the application and explore the interface.
Key Features & Explanation:
- Visual Workflow Creation: Drag-and-drop interface enables users to create data workflow without writing a code. Each workflow consists of nodes, which represent individual steps for data processing or analysis. These nodes are connected to form branching workflows, allowing for seamless data flow and transformation.
- Open Source and Extensible: KNIME is a free and open source platform supported by a large community. It facilitates integration with popular programming languages including Python and Java, as well as libraries like TensorFlow and PyTorch. KNIME also allows users to create custom nodes or modify existing ones using Python or Java scripts. You can add community made extensions or create your own using Java.
- Data Integration and Blending: KNIME can connect different data sources including databases, cloud storage, Excel and CSV files. It also enables data blending, allowing users to merge data from multiple sources for analysis. It also enables users to process and analyse large datasets efficiently.
- Advanced Analytics and Machine Learning: For data preprocessing (Cleaning, transforming, and aggregating data using built-in nodes) to model deployment, KNIME provides built-in tools for Statistical analysis and machine learning, Deep learning and K-AI.
- Reporting and Visualization: It offers tools for creating interactive dashboards, reports and visualization. Users can export results in multiple formats, including pdf, Excel and HTML.
- Collaboration and Deployment: With KNIME server, teams can work together, schedule workflows, and deploy them as REST APIs or web services. This facilitates the sharing and reuse of workflows across teams.
- AI-Powered Suggestions: KNIME utilizes K-AI to suggest workflow optimizations and simplify repetitive processes, increasing its efficiency.
Code Examples:
Creating a New Workflow in KNIME:
- Open KNIME and go to File > New to create a new workflow.
- Provide a name for the workflow and click on Finish.
Setting up 1st workflow in KNIME:
- We will firstly import our dataset using the CSV reader node.
- After running the node, we can see the complete dataset as the output.
- Then we will create a branch from the CSV reader node and attach it to a GroupBy node.
- After clicking on the configure button of the GroupBy node, we will group the data by “Branch” and calculate the mean of the “Total” column.
- After doing this we will create a branch from the “GroupBy” node and attach it to a “Column renamer” node. After clicking on the configure button of this node, we will change the name of the column from “Mean (Total)” to “Average Transaction Value”.
- Then we will attach a branch from the “Column Renamer” node to the “Bar Graph” node that will create a visual representation of the data.
- Then we will do a similar thing and create a branch from “CSV reader” to a GroupBy node. We will group “Product Line”, and calculate the count of the “Quantity” column.
- Then we will create a pie chart using a pie chart node.
- Then we will create another branch from “CSV reader” to a GroupBy node. We will group “Customer Type”, and calculate the mean of the “Total” column.
- Then we will create a bar graph using a bar graph node.
Let’s create the 2nd KNIME Workflow:
- Drag-and-drop a “CSV Reader” node onto the Workflow editor and then configure it to import your csv file.
- Now click on apply, and run the node. You can see the whole dataset as an output.
- Connect an output port of the “CSV Reader” node to the input port of “Missing Value” node.
- Now configure it to set the missing values of:
• Price_per_Quintal → Median
• Demand_Index → Mean
• Other categorical columns → Replace with "Do nothing"
- Add a “Category to Number” node to convert categorical values to numerical.
Apply this transformation to the columns: Market, Crop, Quality_Grade, Transport_Mode, Farmer_Name, and Storage_Facility.
- Add a “Normalizer” node.
Then, apply Z-score normalization that Centers data around the mean with a standard deviation of 1 to:
Price_per_Quintal, Quantity_Available_Tonnes, and Demand_Index.
- Add a “K-Means” node, it clusters data into K groups based on similarity. It is an unsupervised learning algorithm that assigns each data point to the nearest cluster center. Set Number of Clusters to 4.
Use Price_per_Quintal, Quantity_Available_Tonnes, and Demand_Index as clustering attributes.
- Add a “Color Manager” node. Assign different colors to each Cluster for better visualization in reports, charts, and interactive views.
- Add a “Scatter Plot” node to visualize relationships between Price_per_Quintal and Demand_Index.
Set Horizontal-axis to Price_per_Quintal, Vertical-axis to Demand_Index and Color by Cluster.
Use Cases:
- Data Cleaning and preprocessing: Handling missing values and duplicates before the analysis.
- Customer Segmentation: It can be used to analyze the customer data for creating different strategies for marketing.
- Healthcare Analytics: Analyzing patient’s records for identifying disease patterns. It can also optimize the hospital resources.
- Business intelligence: Analyze sales data to identify patterns and potential growth areas. It can create interactive dashboards for decision making.
- Finance: It can be used to detect any discrepancy in financial transactions using its machine learning models. In a way it can act as a fraud detector.
- IoT and Sensor Data Processing: Analyzing real-time data from sensors in industries like manufacturing.
Conclusion:
KNIME is a versatile and powerful platform that provides no code or low code solution for data analytics, Machine learning and automation. Its user-friendly interface and flexibility make it accessible to beginners while also providing advanced features for professionals. Whether you're transforming raw data into actionable insights or optimizing complex workflows, KNIME enables users across industries to unlock the full potential of their data.