Machine Learning in Excel

Vaimal is a machine learning add-in that allows you to train and deploy machine learning algorithms without programming.  You can make predictions on new data using models that are trained on historical data.  Vaimal allows you to create decision trees, support vector machines and neural networks all within Excel®.

It also includes more powerful ensemble methods to combine models for even better predictive performance.  The easy to use interface allows you to focus on your data without worrying about learning mundane programming tasks required with common machine learning platforms.

Preprocessing is fundamental to achieving successful machine learning, and Vaimal has numerous preprocessing tools to simplify the task of getting your data ready for training and prediction.

Order Now

You will be directed to our reseller FastSpring for secure check-out, and immediate download.

Single user permanent license $499.

>License Agreement

Machine Learning Workflow

The workflow for creating and using machine learning models with Vaimal:

  1. Import Data Place data in an Excel worksheet.
  2. Data Preprocessing Handle missing data, data normalization, and encoding categorical inputs. Vaimal has several utilities for preprocessing data.
  3. Select a Model to Use and Design. Select which model to use and the design parameters.
  4. Train the Model Using training data with known outputs, train the model.
  5. Test the Model Using different data than the training data, test the model’s ability to predict versus known outputs.
  6. Prediction Use the model to make predictions of data with unknown output.  A preprocessing template can be used to preprocess data for prediction in one step.


Preprocessing Tools

Data Check

Data check scans your data and alerts you to missing data, non-numeric data, and columns with all values the same.  This is a good initial check to help find data that needs to be cleaned or transformed.

Normalize data

Feature scaling on [0,1]

Feature scaling on [-1,1]


Categorical data encoding

1 to n

1 of c

1 of c-1

1 vs all

Missing data

Replace missing values with artificial or derived data.

Delete rows with missing values or specified value.

Clear cells with non-numeric or specified value.

Variable Importance

Decision trees can be used to determine each input variable's relative importance.  This can be used to eliminate unnecessary data.

Preprocesssing Templates

When your model goes to production, templates allow you to preprocess new data in a single step.  They also alert you to potential problems with missing or invalid data.


Decision trees

Support vector machines


Polynomial, Gaussian, and hyperbolic tangent non-linear kernels

Binary and multi-class classification

Multi-layer perceptrons

Up to 10 hidden layers

ReLU, leaky ReLU, logistic, and hyperbolic tangent hidden layer activation functions

L1 and L2 regularization

Probabilistic neural networks

Generalized regression neural networks

Ensemble Methods

Voting ensembles

Bagging ensembles, including feature bagging for decision trees.

Sampling Methods

Three methods for handling imbalanced class distributions:

Over-sampling  For classes that don’t have the most instances, additional copies of data points are added to the training set.

Under-sampling  For classes that don’t have the least instances, some data points are removed from the training set.

Balance-sampling  The average of the classes with the most and least instances is calculated.  This average is used to over or under sample each class so that all classes have the same number of instances.


Holdout cross-validation

K-fold cross-validation

Leave one out cross-validation


Test after training automatically

Test existing model

Batch prediction

Decision tree visualizer to draw trees.

Function to supply random variable inputs to a machine learning model and simulate output from the model.  The output from a machine learning model can also be used as an input to an analytic model for simulation.  This function is compatible with Simulation Master.


Useful Information

You can download the Vaimal user manual from the following link.

>Vaimal User Manual

There are example workbooks containing machine learning examples for various data sets.  Some examples have application briefs explaining how the machine learning models are set-up and used.

>Machine Learning Examples

Visit the Knowledge Base for information on all of our products.  You can find tutorials, videos, and articles on using and applying our products.

>Knowledge Base


$499 for single user permanent license.

30 day money back guarantee.

One year of product support.  For support, visit the Support page.

One year of product updates.

Volume discounts available.  Inquire at

Refer to this page for purchase orders.

We strive to make our software universally compatible with Excel 2007 through 2019, and all active Windows versions.  It is strongly recommended that you test your system with the free trial version prior to purchasing.  Besides, we want you to try the software so you are comfortable with your purchase.

Excel for Mac not supported at this time.

Excel is a registered trademark of Microsoft Corporation.