Optbinning

New additions:

- Class ``OptBinning`` introduces a new constraint to reduce dominating bins, using parameter ``gamma``.
- Metrics HHI, HHI regularized and Cramer's V added to ``binning_table.analysis`` method. Updated quality score.
- Added column min/max target and zeros count to ``ContinuousOptimalBinning`` binning table.
- Binning algorithms support univariate outlier detection methods.

Tutorials:

- Tutorial: optimal binning with binary target. New section: Reduction of dominating bins.
- Enhance binning process tutorials.

New additions:

- Binning process to support optimal binning of all variables in dataset.
- Add ``print_output`` option to ``binning_table.analysis`` method.
- New unit tests added.

Tutorials:

- Tutorial: Binning process with Scikit-learn pipelines.
- Tutorial: FICO Explainable Machine Learning Challenge using binning process.

Bugfixes:

- Fix ``OptBinning.information`` print level default option.
- Avoid numpy.digitize if no splits.
- Compute Gini in ``binning_table.build`` method.

Bugfixes:

* Fix a bug in ``OptimalBinning.fit_transform`` when calling ``tranform`` internally.
* Replace np.int by np.int64 in ``model_data.py`` functions to guarantee 64-bit integer on Windows.
* Fix a bug in ``_chech_metric_special_missing``.

First release of OptBinning.