A Data Science tool: KNIME

What is Knime and what is it used for?

I decided to write this article to introduce, or rather to remind myself, of the data tool that I used for some checks during a project at one of my previous companies for about 6 months and while writing my master’s thesis.

What is KNIME?

KNIME offers a complete platform for end-to-end data science, from creating analytic models, to deploying them and sharing insights within the organization, through to data apps and services.

KNIME (Konstanz Information Miner) is an open source data analytics platform for data analysis, data discovery and data science. Developed at the University of Konstanz in Switzerland, KNIME includes a set of tools and modules to perform data analysis operations via a visual user interface.

KNIME is a powerful tool that facilitates data science and analysis processes and appeals to users of all levels thanks to its user-friendly interface. Additionally, being open source encourages community-based development and sharing, thus enabling the sharing of knowledge and experience among users.

Features and Functions:

1. Visual Programming Interface: KNIME offers a visual programming interface that enables creating workflows via drag and drop. Users can use this interface to organize, connect and structure their data analysis processes.
2. Extensibility: KNIME offers many plugins and integrations, including various data processing, analytics, and machine learning modules. Users can extend the platform by installing and configuring these modules according to their needs.
3. Data Preparation: KNIME provides a set of tools and functions to perform data preprocessing steps. Operations such as data cleaning, conversion, merging and filtering can be easily performed.
4. Data Analysis and Visualization: KNIME includes tools to apply various statistical analysis techniques and visually present the results. Users can use the platform to understand and interpret data by creating graphs, tables, and reports.
5. Machine Learning: KNIME offers a set of modules that include various machine learning algorithms. Users can use these modules to perform machine learning tasks such as classification, regression, clustering, and dimensionality reduction.
6. Big Data Support: Provides integration with KNIME, Apache Hadoop, Apache Spark and other big data technologies. In this way, it can perform data analysis and processing on large data sets.

Some Example Usage Areas:

• Biomedical Research and Health Sciences
• Finance and Insurance
• Retail and Customer Analytics
• Manufacturing and Industrial Enterprises
• Academic Research and Education

Is Knime a Low-Code/No-Code tool?

KNIME is not strictly described as a “low-code” or “no-code” platform, but more of a data analytics platform for data analytics, data science, and machine learning. Low-code or no-code platforms are generally designed to speed up application development processes and make it easier to create applications without the need for coding skills.

KNIME allows users to perform data analysis and data processing through a visual interface. However, this platform is often used to support complex data processing, analysis and modeling processes, and often users may need to have a basic programming skill. Therefore, KNIME is considered more of a data-driven tool rather than low-code platforms.

A Sample Application with Knime

If you want to make a sample application with KNIME, you can choose a project focused on data analysis or machine learning. Here is an example application you can make using KNIME:

Project Name: House Price Estimation

Purpose: This project aims to create a machine learning model to predict house prices.

Steps:

1. Data Collection: As a first step, you will need to find a data set that includes home prices. For example, you can download a dataset from platforms like Kaggle.
2. Data Preprocessing: Using KNIME, load your dataset and perform preprocessing steps. These steps may include filling in missing data values, coding categorical data, and removing unnecessary features.
3. Data Exploration and Visualization: Learn more about data by visualizing your dataset and performing statistical analysis. You can better understand the data thanks to the various visualization tools offered by KNIME.
4. Model Building: Build a house price prediction model with the machine learning tools included in KNIME. In this step, you can choose the model that gives the best performance by trying different algorithms.
5. Model Evaluation: Test the model you created and evaluate its performance. You can analyze the accuracy and performance of the model with various metrics offered by KNIME.
6. Application Development: Finally, develop an application using KNIME Workflow. This app may have an interface where the user enters home specifications and then displays an estimated price.

This sample project is an application that can be implemented using KNIME’s data analysis and machine learning capabilities. Thanks to the visual interface offered by KNIME, it is very easy to follow and understand the data analysis and modeling processes step by step.

Sources and licensing fees:
Pricing: https://www.knime.com/knime-hub-pricinghttps://www.knime.com/software-overview

I wish everyone happy coding!

May the Force be with you…🚀