docs: 📝 simplify datathons guide and remove outdated resources

The lengthy checklist and resources sections were removed in favor of a concise overview focused on key benefits. Original content was moved to the datathon-kit repo.

Datathons.md

@@ -1,75 +1,5 @@
 # Datathons
 
-## Checklist
-
-1. Learn more about the problem. Search for similar Kaggle competitions. Check the task in [Papers with Code](https://paperswithcode.com/). Check [Machine Learning subreddit](https://www.reddit.com/r/MachineLearning) for similar problems.
-2. Do a basic data exploration. Try to understand the problem and gather a sense of what can be important.
-3. Get baseline model working. You can also rely on things like [TableVectorizer](https://skrub-data.org/stable/reference/generated/skrub.TableVectorizer.html#tablevectorizer).
-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.
-5. Try different approaches for preprocessing (encodings, Deep Feature Synthesis, lags, aggregations, imputers, target/count encoding...). If you're working as a group, split preprocessing feature generation between files.
-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.
-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.
-8. Tune hyper-parameters once you've settled on an specific approach ([hyperopt](target distribution), [optuna](https://optuna.readthedocs.io/)).
-9. Plot and visualize the predictions (target vs predicted errors, histograms, random prediction, ...) to make sure they're doing as expected. Explain the predictions with [SHAP](https://github.com/slundberg/shap).
-10. Think about what postprocessing heuristics (clipping, mapping to another distribution, ...) can be done to improve or correct predictions.
-11. [Stack](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html) classifiers ([example](https://www.kaggle.com/couyang/featuretools-sklearn-pipeline#ML-Pipeline)).
-12. Try AutoML models.
-  - Tabular: [AutoGluon](https://auto.gluon.ai/), [AutoSklearn](https://github.com/automl/auto-sklearn), Google AI Platform, [PyCaret](https://github.com/pycaret/pycaret), [Fast.ai](https://docs.fast.ai/).
-  - Time Series: [AtsPy](https://github.com/firmai/atspy), [DeepAR](https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-recipe-deeparplus.html), [Nixtla's NBEATS](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeats.html), [AutoTS](https://github.com/winedarksea/AutoTS).
-
-## Preprocessing Resources
-
-- [Feature Engineering Library](https://feature-engine.trainindata.com/).
-- [Feature Engineering Ideas](https://github.com/aikho/awesome-feature-engineering).
-- [Deep Feature Synthesis](https://featuretools.alteryx.com/en/stable/getting_started/afe.html). [Simple tutorial](https://www.kaggle.com/willkoehrsen/automated-feature-engineering-basics).
-- [Modern Feature Engineering Ideas](https://www.kaggle.com/c/playground-series-s4e12/discussion/554328) ([code](https://www.kaggle.com/code/cdeotte/first-place-single-model-cv-1-016-lb-1-016)).
-  - [Target Encoding](https://www.kaggle.com/competitions/playground-series-s4e12/discussion/554328) (with cross-validation to avoid leakage). [Data leakage is a common problem in Target Encoding](https://www.geeksforgeeks.org/target-encoding-using-nested-cv-in-sklearn-pipeline/#the-challenge-of-data-leakage-nested-crossvalidation-cv)!
-  - Forward Feature Selection.
-- [Hillclimbing](https://www.kaggle.com/competitions/playground-series-s3e14/discussion/410639).
-
-## Exploratory Data Analysis Resources
-
-- [HiPlot](https://facebookresearch.github.io/hiplot/)
-
-### Scikit Learn Compatible Transformers
-
-- [LEGO](https://github.com/koaning/scikit-lego)
-- [Skrub](https://github.com/skrub-data/skrub)
-- [Skoot](https://github.com/tgsmith61591/skoot)
-- [Sktools](https://github.com/david26694/sktools)
-- [Scikit-Learn Related Projects](https://scikit-learn.org/stable/related_projects.html).
-
-### Other Compatible Tools
-
-- [Contributions repository](https://github.com/scikit-learn-contrib)
-- [Awesome Scikit-Learn](https://github.com/fkromer/awesome-scikit-learn)
-
-### Polars
-
-- [Modern Polars](https://kevinheavey.github.io/modern-polars/)
-- [Polars The Definitive Guide](https://github.com/jeroenjanssens/python-polars-the-definitive-guide)
-
-## Time Series Resources
-
-- [Quick Tutorials](https://www.kaggle.com/c/jane-street-market-prediction/discussion/198951)
-- [Tsfresh](https://tsfresh.readthedocs.io/en/latest/)
-- [Fold](https://github.com/dream-faster/fold)
-- [Neural Prophet](https://neuralprophet.com/) or [TimesFM](https://github.com/google-research/timesfm)
-- [Darts](https://github.com/unit8co/darts)
-- [Functime](https://docs.functime.ai/)
-- [Pytimetk](https://github.com/business-science/pytimetk)
-- [Sktime](https://github.com/alan-turing-institute/sktime) / [Aeon](https://github.com/aeon-toolkit/aeon)
-- [Awesome Collection](https://github.com/MaxBenChrist/awesome_time_series_in_python)
-- [Video with great ideas](https://www.youtube.com/watch?v=9QtL7m3YS9I)
-- [Tutorial Kaggle Notebook](https://www.kaggle.com/code/tumpanjawat/s3e19-course-eda-fe-lightgbm)
-- Think about adding external datasets like [related Google Trends search](https://trends.google.com/trends/), PiPy Packages downloads, [Statista](https://www.statista.com/), weather, ...
-- [TabPFN for time series](https://github.com/liam-sbhoo/tabpfn-time-series)
-
-## Datathon Platforms
-
-- [Kaggle](https://www.kaggle.com/competitions)
-- [MLContest](https://mlcontests.com/). They also share a "State of Competitive Machine Learning" report every year ([2023](https://mlcontests.com/state-of-competitive-machine-learning-2023)) and summaries on the state of the art for ["Tabular Data"](https://mlcontests.com/tabular-data/).
-- [Humyn](https://app.humyn.ai/)
-- [DrivenData](https://www.drivendata.org/competitions/)
-- [Xeek](https://xeek.ai/challenges)
-- [Cryptopond](https://cryptopond.xyz/)
+- Datathons are a great way to learn new skills and get experience in a short amount of time.
+- Doing datathons helps you keep up with the latest trends and technologies.
+- Have a [Datathon Kit](https://github.com/davidgasquez/datathon-kit) ready to go to get started quickly.