Mljar-supervised

Enhancements
- 523 Add type hints to AutoML class, thank you DanielR59
- 519 save train&validation index to file in train/test split, thanks filipsPL MaciekEO

Bug fixes
- 496 fix exception in baseline mode, thanks DanielR59 moshe-rl
- 522 fixed requirements issue, thanks DanielR59 MaciekEO
- 514 remove warning, thanks MaciekEO
- 511 disable EDA, thanks MaciekEO

Bug fixes
- 463 change multiprocessing to Parallel with loky
- 462 handle large data for tree visualization in regression
- 419 remove/hide warnings
- 411 loose dependencies for numpy and scipy

Enhancements
- 81 add scatter plot predicted vs target in regression
- 158 add ROC curve for binary classification
- 336 add visualization for Optuna results
- 352 add support for Colab
- 374 update seaborn
- 378 set golden features number
- 379 switch off boost_on_errors step in Optuna mode
- 380 add custom cross validation strategy
- 386 add correlation heatmap
- 387 add residual plot
- 389 add feature importance heatmap
- 390 add custom eval metric
- 393 update sklearn

Bug fixes
- 308 fix error in kaggle kernel
- 353, 355, 366, 368, 376, 382, 383, 384 fixes

Docs
- 391 add info about hyperparameters optimization methods

Big thank you for help for: ecoskian, xuzhang5788, xiaobo, RafaD5, drorhilman, strelzoff-erdc, muxuezi, tresoldi THANK YOU !!!

Enhancements
- 343 set seed in Optuna
- 344 set eval_metric directly in all algorithms
- 350 add estimated train time in Optuna mode
- 342 add `optuna_verbose` param in `AutoML()`
- 354 add KNN in Optuna
- 356 and Neural Network in Optuna
- 357, 348 use mljar wrapper for Random Forest and Extra Trees
- 358 add `extra_tree` param in LightGBM
- 359 switch off feature engineering in Optuna mode - only highly tuned models are produced
- 361 list all `eval_metric` in error message
- 362 add accuracy `eval_metric`
- 340 support for r2

Bug fixes
- 347 dont include Optuna tuning time in `total_time_limit`
- 360 missing auc scores for training in CatBoost

Add support to Python 3.9 (339) Thanks to rterbush!

Enhancements

- 332 We added Optuna framework for hyperparameters tuning. It can be used by setting `mode="Optuna"` in AutoML. You can read more details at blog post: https://mljar.com/blog/automl-optuna/