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Data/Analytics Engineering.md
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Data/Analytics Engineering.md
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- [Flipside Crypto](https://github.com/FlipsideCrypto/external-models)
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- [MetricsDAO](https://github.com/MetricsDAO)
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- [RA Analytics](https://github.com/rittmananalytics/ra_data_warehouse)
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- [Anomstack](https://github.com/andrewm4894/anomstack)
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### Related [[Podcasts]]
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Datathons.md
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Datathons.md
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3. Get baseline model working.
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4. Design an evaluation method as close as the final evaluation. Plot local evaluation metrics against the public ones (correlation) to validate how well your validation strategy works.
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5. Try different approaches for preprocessing (encodings, Deep Feature Synthesis, lags, aggregations, imputers, ...). If you're working as a group, split preprocessing feature generation between files.
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6. Plot learning curves ([sklearn](https://scikit-learn.org/stable/modules/learning_curve.html) or [external tools](https://github.com/reiinakano/scikit-plot)) to avoid overfitting.
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6. Plot learning curves ([sklearn](https://scikit-learn.org/stable/modules/learning_curve.html) or [external tools](https://github.com/reiinakano/scikit-plot)) to avoid overfitting.
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7. Plot real and predicted target distribution to see how well your model understand the underlying distribution. Apply any postprocessing that might fix small things.
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8. Tune hyper-parameters once you've settled on an specific approach ([hyperopt](target distribution), [optuna](https://optuna.readthedocs.io/)).
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9. Plot and visualize the predictions (histograms, random prediction, ...) to make sure they're doing as expected. Explain the predictions with [SHAP](https://github.com/slundberg/shap).
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- [Sktime](https://github.com/alan-turing-institute/sktime) / [Aeon](https://github.com/aeon-toolkit/aeon)
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- [Awesome Collection](https://github.com/MaxBenChrist/awesome_time_series_in_python)
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- [Video with great ideas](https://www.youtube.com/watch?v=9QtL7m3YS9I)
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- [Tutorial Kaggle Notebook](https://www.kaggle.com/code/tumpanjawat/s3e19-course-eda-fe-lightgbm)
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- [Tutorial Kaggle Notebook](https://www.kaggle.com/code/tumpanjawat/s3e19-course-eda-fe-lightgbm)
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## Datathon Platforms
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- [Kaggle](https://www.kaggle.com/competitions)
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- [MLContest](https://mlcontests.com/)
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- [Humyn](https://app.humyn.ai/)
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- [DrivenData](https://www.drivendata.org/competitions/)
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- [Xeek](https://xeek.ai/challenges)
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Open Data.md
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Open Data.md
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- [Victoriano's Data Sources](https://victorianoi.notion.site/Data-Sources-79b28912c6d941af99e6ef102c578fa0)
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- [Data is Plural](https://www.data-is-plural.com/)
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- [Public APIs](https://github.com/public-api-lists/public-api-lists)
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- [Real Time Datasets](https://github.com/bytewax/awesome-public-real-time-datasets)
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## Open Source Web Data IDE
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