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JuliaAI / DataScienceTutorials.jl

A set of tutorials to show how to use Julia for data science (DataFrames, MLJ, ...)
https://juliaai.github.io/DataScienceTutorials.jl/
MIT License
108 stars 18 forks source link

Add this kaggle attempt to external resources #45

Open tlienart opened 4 years ago

tlienart commented 4 years ago

https://github.com/stefanjwojcik/mm2020/issues/1

tlienart commented 4 years ago

Basically we should just link to it. It would give more creds to original author + there's a fair bit of pre-proc code and it relies on a dataset that we can't serve.

ablaom commented 4 years ago

it relies on a dataset that we can't serve.

Oh well.

A link would be good.

azev77 commented 4 years ago

In general it would be nice to have a link to as many MLJ codes for ML datasets which are also featured on Kaggle: -Note: many datasets below (Boston, Ames, MNIST etc) do not belong to Kaggle -Some of these links are competitions, others just practice, some ongoing, some not Boston: https://www.kaggle.com/c/boston-housing Ames: https://www.kaggle.com/c/house-prices-advanced-regression-techniques Titanic: https://www.kaggle.com/c/titanic MNIST: https://www.kaggle.com/c/digit-recognizer (ps: I don't think MLJ has @load MNIST @ablaom @tlienart this could be helpful)

Iris: https://www.kaggle.com/uciml/iris Pima: https://www.kaggle.com/uciml/pima-indians-diabetes-database King County: https://www.kaggle.com/harlfoxem/housesalesprediction/kernels https://rdrr.io/cran/moderndive/man/house_prices.html