- [ ] setup.py
https://godatadriven.com/blog/a-practical-guide-to-using-setup-py
https://python-packaging-tutorial.readthedocs.io/en/latest/setup_py.html
- [ ] descriptives
- [ ] working with dates
https://calmcode.io/pandas-datetime/introduction.html
https://datacamp.com/courses/working-with-dates-and-times-in-python
- [ ] visualizations
- [ ] correlations
- [ ] explore clusters
- [x] make_pipeline
- [x] missing imputation
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
- [x] feature reduction
- [x] feature selection
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html
https://scikit-learn.org/stable/modules/feature_selection.html
- [ ] scaling
- [ ] balancing
- [x] hyperparameter tuning
https://scikit-optimize.github.io/stable/
- [x] gridsearchcv w/ auc / accuracy
- [ ] multiclass model
- [ ] response model
- [ ] uplift model
https://towardsdatascience.com/beyond-churn-an-introduction-to-uplift-modeling-d1d9af7be
- [ ] lightgbm
https://www.kaggle.com/code/prashant111/lightgbm-classifier-in-python/notebook
https://lightgbm.readthedocs.io/en/latest/Features.html
https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc
- [ ] tackle imbalance
https://www.datacamp.com/community/tutorials/diving-deep-imbalanced-data
https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/
- [x] classification report / explain and follow up strategies
- [ ] scalers + explain impact on model
- [x] identify overfitting
- [ ] roc, precision-recall curve
https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/
https://modelplot.github.io/intro_modelplotpy.html
- [ ] post processing
- [ ] visual presentation of model
https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html
https://www.scikit-yb.org/en/latest/index.html
- [ ] shap
https://www.kaggle.com/code/dansbecker/shap-values
https://shap.readthedocs.io/en/latest/
https://towardsdatascience.com/introduction-to-shap-with-python-d27edc23c454
- [ ] decorators
https://calmcode.io/decorators/introduction.html
https://www.youtube.com/watch?v=FsAPt_9Bf3U
- [ ] type hinting
https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html
https://realpython.com/lessons/type-hinting/
https://www.pythonsheets.com/notes/python-typing.html
- [x] github actions
https://github.com/skills/publish-packages
https://github.com/sdras/awesome-actions
https://docs.docker.com/ci-cd/github-actions/
https://www.youtube.com/watch?v=oi94qEvi9Qo
https://github.com/myautoml/myautoml/blob/develop/.github/workflows/python-publish.yml
- [ ] proviling
https://calmcode.io/pyinstrument/introduction.html
- [ ] tox
- [ ] unit test
https://hypothesis.readthedocs.io/en/latest/
https://madewithml.com/courses/mlops/testing/#pytest
https://realpython.com/python-testing/
- [ ] type hinting w/
https://realpython.com/lessons/type-hinting/
https://www.pythonsheets.com/notes/python-typing.html
https://mypy.readthedocs.io/en/stable/getting_started.html
- [ ] deploy w/ docker
https://udemy.com/course/learn-docker/
https://madewithml.com/courses/mlops/docker/
https://calmcode.io/til/docker-prune.html
https://madewithml.com/courses/mlops/docker/
- [ ] notes on fairness, privacy
- [ ] notes on business kpi's
- [ ] garbage collection
https://towardsdatascience.com/memory-management-and-garbage-collection-in-python-c1cb51d1612c