We want you to be happy to pay us!

Predictive modeling is a complex science. But what is more frustrating that obtaining poor or no results at all after having invested time and money in a data mining project? At DNAlytics, we clearly understand this. On our side, it is also a pity to have to announce such poor project outcome to our customers. We definitely don't like it. That is why we now propose a very fast evaluation of the potential value of your data, and this for free!

What does REED do ?

REED (Rapid and Easy Evaluation of Datasets) is a web application which aims at automatically process a dataset in order to get a quick guess of the interest the data represents in terms of predictive modeling and markers identification. The idea is not at all to perform our best work, which cannot be automated, but to give to prospects some hints about their data potential and also some specific issues that the data would contain, and that should be looked at in details. In particular, REED provides:

• Estimation of predictive performances either with all available features or a good scenario with a smaller number of features automatically selected by our algorithms.
• The number of features that we judge necessary to transform or drop because of their particular behavior.
• An outlier analysis, suggesting that some samples might be considered for removal before processing the data further.
• The number of features statistically significantly differing from one diagnosis to the other.

To begin, enter your email address and upload a dataset

After the upload, please validate the data import (see left column). You will then receive an email as soon as the results are available.

Expected file format : Comma-separated values

• Categorical values are surrounded by double quotes ( " ).
• Numerical values are encoded without quote.
• The decimal separator is a dot ( . ).
• Missing values are encoded as empty fields (see third sample of the example below) or NA without quote.
• The first line begins with an empty field and then encodes the variable names.
• The first column encodes sample identifiers.
• For classification, the target variable is named class.
• For regression, the target variable is named response.
• The target variable cannot contain missing values.

Here is an example of the expected file format. It is a subsamble of the Iris dataset. Spaces are optional.

     ,"Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","class"
 "x1",           5.1,          3.5,           1.4,          0.2,"setosa"
 "x2",           4.9,            3,           1.4,          0.2,"setosa"
 "x3",           4.7,          3.2,              ,          0.2,"setosa"
 "x4",           4.6,          3.1,           1.5,          0.2,"setosa"
"x51",             7,          3.2,           4.7,          1.4,"versicolor"
"x52",           6.4,          3.2,           4.5,          1.5,"versicolor"
"x53",           6.9,          3.1,           4.9,          1.5,"versicolor"
"x54",           5.5,          2.3,             4,          1.3,"versicolor"

Some demo datasets are available in the Help section.