Minitab, LLC, a market leader in data analysis, predictive analytics and process improvement, announced the launch of new predictive analytics capabilities and advanced machine learning methods in Minitab Statistical Software. In addition to Minitab’s classical methods, users can now leverage the power of advanced machine learning methods through clicks by deploying Minitab’s new predictive analytics module or code, by integrating with open-source languages R or Python.

With Minitab’s new predictive analytics module, users will be able to solve more challenging problems, tap into deeper insights and visualize complex interactions in a better, faster, easier, and more accurate way. Skillfully predict, compare alternatives and forecast with ease using Minitab’s revolutionary predictive analytics techniques.

Also Read: Implementing Machine Learning for Data Analysis

Minitab’s predictive analytics module consists of proprietary methods such as Classification and Regression Trees (CART®), the original Random Forests®, a classification algorithm consisting of many decision trees and TreeNet®, Minitab’s own gradient boosting methodology. Developed by the inventors of tree-based modelling techniques, Minitab is the only company in the world to offer these branded and popular methods. Now, Minitab is making these methods accessible to everyone, not just data scientists, no matter where they are on their analytics journey.

Jeffrey T. Slovin, President and Chief Executive Officer of Minitab, said: ‘Minitab’s predictive analytics module underscores our commitment to helping organisations accelerate their transformations. By making advanced machine learning easy-to-use and understand, companies around the globe can access the power of these methods to solve complex problems and predict outcomes better, faster and easier than ever before.’