Power your decisions with accurate predictive analytics

Try instant data modeling and forecasting with top-rated analytics software from Minitab

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Minitab ranked 2022 Top Performer for Data analysis and Visualization Software and Established Player for Statistical Analysis Software by Capterra Minitab rated Best Statistical Analysis Software for Data Scientists by Software Suggest

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4 reasons to get started for free immediately:

  • Identify trends and opportunities in your data, without coding or statistical experience
  • Easy-to-use data models quickly determine the key predictors for your goals
  • Award-winning decision-tree algorithms provide accurate results you can trust
  • Machine Learning methods you can easily understand, visualize and execute
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CART® (Classification & Regression Decision Trees)

One of the most popular tools in modern data mining, this tree-based algorithm discovers how to split data into smaller segments, then selects the best performing splits repeatedly until an optimal collection is found.

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Random Forests® (Machine Learning model)

Based on a collection of CART Trees, this algorithm uses repetition, randomization, sampling, and ensemble learning while simultaneously bringing together independent trees to determine the overall prediction of the forest.

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TreeNet® (Gradient Boosting algorithm)

Our most flexible, award-winning and powerful machine learning tool is known for its superb and consistent predictive accuracy due to its iterative structure that corrects combined errors of the ensemble as it builds.

Predictive and Prescriptive Analytics Tools
Decision Trees - Supervised Algorithms
Decision Trees - Unsupervised Algorithms
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"TreeNet made it very simple for us to hone in on the key predictors and be able to devise strategies to be able to deal with those effectively."

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"Thanks to TreeNet, the bank was able to identify banking customers they could lost to another bank with an accuracy of between 80% to 90%."

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"CART® compared data from practitioners caring for patients with SARS to data from practitioners who were not exposed to the virus, to identify risk factors for SARS-CoV transmission."

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