Data-science-utils

Changed
- update packages and supported python version
Fixed
- minor changes

Update requirements
Remove unnecessary warnings by seaborn and matplotlib
Update tests
Update version
Add support for python 3.9 and remove support form 3.6 and 3.7
Fix bug for empty data frame return in preprocess::get_correlated_features

Added
- xai::plot_features_importance method that visualize into bar chart the feature importance.
- a new module named `unsupervised` was added. The module contains methods that calculate and/or visualize evaluation
performance of an unsupervised model.
- unsupervised::plot_cluster_cardinality method that plots the number of points per cluster as a bar chart.
- unsupervised::plot_cluster_magnitude method that plots the Total Point-to-Centroid Distance per cluster as a bar
chart.
- unsupervised::plot_magnitude_vs_cardinality method plots the cardinality vs. magnitude as a scatter plot.
- unsupervised::plot_loss_vs_cluster_number method that plots the graph which helps to find the optimum parameter ``k``
for KMeans.
Changed
- deprecated xai::draw_tree. Use sklearn.tree.plot_tree instead.
- requirements dependencies.
Fixed
- minor changes

Added
- code examples to README.md
Changed
- visualization_aids module was merged into the preprocess module.
Fixed
- avoid FutureWarning due to sklearn version upgrade (Pass labels=[1, 0], pos_label=0, average=binary, sample_weight=None as keyword args. From version 0.25 passing these as positional arguments will result in an error).
- fixed docs
- minor changes

Added
- visualization_aids::visualize_feature method that visualize one feature distribution.
- metrics::visualize_accuracy_grouped_by_probability method that visualize accuracy stacked by probability.
Changed
- visualization_aids::visualize_features was deprecated.
Fixed
- Ravel y_train in metrics::plot_metric_growth_per_labeled_instances if the shape is (n_sample, 1) to avoid DataConversionWarning (A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().)
- minor changes