Refactored all the engine to work with pandas.
Changed normalization to scaling.
Types are infered from the dataset so they don't have to be specified in the configuration file.
Cleaned up parts of the code that are no longer used since the pandas transition.
Added splitting the dataframe into testing and training data (that's the new output of the abris class).
The split percentage is loaded from a configuration file.

Improved parameter parsing so anything that is not a string can be handled as well.

Boolean mapping now uses a Binarizer instead of a NullTransformer, which makes it more stable.
NllTransformer -> AsNumpyArrayTransformer.
This change was done in order to allow the framework to work with dataframes as input as well as with numpy arrays.
Extracted the transformation part into a new class (Transformer).
Added ignore keyword.
Added cleaner.
Added basic nan treatment (mean and drop_rows only).
Allowed the target argument to be categorical (assigns a different number to every different class, starting from zero).

[Bugfix] Dataframe index is now reset whenever a row is dropped.
Minor refactoring + Import cleaning.
Input data source to abris can now be either a file like object or a pandas.DataFrame.
Added more methods to nan treatment: median and mode.
Nan treatment now supports categorical missing values in numeric modes using the mode.
Continuous keyword on the configuration file changed to Numeric.

Added a description of the data model to the readme.
Moved Features to a new file.
Cleaner now trims text features.