---
title: Easy-to-use, accurate Predictive Analytics and Machine Learning models
description: Generate accurate predictions and enable data-driven decisions in a few simple clicks. Discover Minitab's easy-to-use Machine Learning models and Predictive Analytics.
image: https://info.minitab.com/hubfs/MSS20%20Cloud%20Launch/MSS%20cloud%20and%20desktop%20WBN/Minitab%20cloud%20and%20desktop%20on%20mobile%20device.gif
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## Power your decisions with accurate predictive analytics

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

![](https://play.vidyard.com/ZNNhkEqgc88nn9CDFv1hqk.jpg)

| ![capterra-shortlist-2022-tanaza-300x300](https://info.minitab.com/hs-fs/hubfs/capterra-shortlist-2022-tanaza-300x300.jpg?width=100&height=100&name=capterra-shortlist-2022-tanaza-300x300.jpg) | ![sotware suggest-300x300](https://info.minitab.com/hs-fs/hubfs/sotware%20suggest-300x300.jpg?width=100&height=100&name=sotware%20suggest-300x300.jpg) |
| --- | --- |
| *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* |

### Start your free trial now

See how easy it is to make accurate predictions for yourself – sign up for instant access

## 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

![whiteboard-asq-lss-background](https://info.minitab.com/hubfs/raw_assets/public/Landin-Page-Templates/images/template-images/minitab-form-1.png)

![Better-icon](https://info.minitab.com/hubfs/Better-icon.png)

## 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.

![Better-whatsnewicon](https://info.minitab.com/hubfs/B2BML%20-%20NEW%20FILE%20MANAGER%20STRUCTURE/Email%20Images/Americas%20/MSS%2020.2%20+%20PA%20Module%20Launch%20-%20April%202021/Better-whatsnewicon.png)

## 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.

![Better-icon](https://info.minitab.com/hubfs/B2BML%20-%20NEW%20FILE%20MANAGER%20STRUCTURE/Email%20Images/Americas%20/MSS%2020.2%20+%20PA%20Module%20Launch%20-%20April%202021/Better-icon.png)

## 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

**Preventive Maintenance**

\- Regression with Life Data  
\- Warranty Prediction  
\- Accelerated Life Testing

**Predictive Analytics in Retail:**

\- Clustering  
\- Modelling Customer behaviour  
\- Time Series Methods (ARIMA)

**Wait Time, Treatment Costs, Satisfaction Forecasting in Healthcare:**

\- Fitted Line Plot  
\- Binary Fitted Line Plot  
\- Fit Regression Model  
\- Binary Logistic Regression  
\- CART Regression  
\- CART Classification

**Fraud prevention, Credit Scoring, Customer Segmentation, Demand prediction in Finance and Insurance:**

\- Multivariate analysis to forecast Consumer behavior  
\- Clustering  
\- Decision Trees  
\- Factorial analysis  
\- Principal component analysis (PCA)  
\- Random Forests and TreeNet Gradient boosting

##### Decision Trees - Supervised Algorithms

**Machine Learning and Modeling Techniques in Data Mining - Supervised Algorithms**

**Classification:**

\- Linear Discriminant Analysis (LDA)  
\- Quadratic Discriminant Analysis (QDA)  
\- Logistic Regression  
\- Classification Trees

**Regression:**

\- Simple  
\- Polynomial  
\- Multiple  
\- Nonlinear  
\- Partial Least Squares  
\- Regression Trees  
\- Regression with Life Data  
\- Warranty Prediction

**Time Series Methods**

\- Trend analysis  
\- Autoregressive Integrated Moving Average (ARIMA)  
\- Moving average  
\- Autocorrelation  
\- Exponential smoothing  
\- Decomposition  
\- Cross correlation  
\- Warranty Prediction

##### Decision Trees - Unsupervised Algorithms

**Machine Learning and Modeling Techniques in Data Mining - Unsupervised Algorithms**

**Clustering:**  
\- Cluster Observations  
\- Cluster Variables  
\- Cluster K-means  
\- Factor Analysis

**Data Reduction:**

\- Principal Component Analysis  
\- Factor Analysis

**Automated Machine Learning**

**Discover the best model for your data:**  
\- CART®  
\- Random Forests®  
\- TreeNet® Gradient Boosting  
\- ROC curve  
\- Relative Variable Importance

![bulb-icon](https://info.minitab.com/hubfs/1_Backgrounds-Icons/bulb-icon.png)

"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."

![team growth-icon](https://info.minitab.com/hubfs/1_Backgrounds-Icons/team%20growth-icon.png)

 "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%."

![chart-icon](https://info.minitab.com/hubfs/1_Backgrounds-Icons/chart-icon.png)

"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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## Ready to make your 1st prediction?

 Start your trial of Minitab Statistical Software

[Start Your Trial](https://info.minitab.com/en/easy-to-use-accurate-predictive-analytics-and-machine-learning-models?hsLang=en#free-trial)

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