ncaa-march-madness-2020

The goal of ncaa-march-madness-2020 is to store the notebooks for this Kaggle Competition, see GitBook including

• Baseline
• XGBOOST 超参数调整
• Target encoding
• ID embedding
• GBDT + LR
• GBDT + LR k-fold
• 变量重要性
• Linear vs. Tree linear?
• Auto-encoder 查询异常值
• Python 包说明

We publish our package with some internal functions, install with

pip install ncaa-march-madness-2020

How to use

All notebooks work in the analysis directory, and save all data files in input, output and data directories.

fs::dir_tree("analysis", recurse = TRUE, regexp = "ipynb")
#> analysis
#> +-- baseline.ipynb
#> +-- evaluate-features.ipynb
#> +-- gbdt_lr.ipynb
#> +-- gbdt_lr_CV.ipynb
#> +-- id2vec.ipynb
#> +-- linear-base-learner.ipynb
#> +-- march-madness-2020-ncaam-simple-lightgbm-on-kfold.ipynb
#> +-- Obtain_Answer.ipynb
#> +-- outliers.ipynb
#> +-- params_tuning.ipynb
#> +-- paris-madness.ipynb
#> +-- pkg_test.ipynb
#> \-- target-encoding.ipynb
fs::dir_tree(recurse = TRUE, regexp = "input|output|data")
#> .
#> +-- data
#> |   +-- feature_importances.csv
#> |   +-- id2vec.npy
#> |   +-- NCAA2020_Kenpom.csv
#> |   +-- outlier_df.csv
#> |   +-- submission_True.csv
#> |   +-- team_strength_embedding.csv
#> |   +-- Tourney_Reuslt.csv
#> |   \-- Tourney_Reuslt_inputs.csv
#> +-- input
#> |   +-- google-cloud-ncaa-march-madness-2020-division-1-mens-tournament
#> |   |   +-- MDataFiles_Stage1
#> |   |   |   +-- Cities.csv
#> |   |   |   +-- Conferences.csv
#> |   |   |   +-- MConferenceTourneyGames.csv
#> |   |   |   +-- MGameCities.csv
#> |   |   |   +-- MMasseyOrdinals.csv
#> |   |   |   +-- MNCAATourneyCompactResults.csv
#> |   |   |   +-- MNCAATourneyDetailedResults.csv
#> |   |   |   +-- MNCAATourneySeedRoundSlots.csv
#> |   |   |   +-- MNCAATourneySeeds.csv
#> |   |   |   +-- MNCAATourneySlots.csv
#> |   |   |   +-- MRegularSeasonCompactResults.csv
#> |   |   |   +-- MRegularSeasonDetailedResults.csv
#> |   |   |   +-- MSeasons.csv
#> |   |   |   +-- MSecondaryTourneyCompactResults.csv
#> |   |   |   +-- MSecondaryTourneyTeams.csv
#> |   |   |   +-- MTeamCoaches.csv
#> |   |   |   +-- MTeamConferences.csv
#> |   |   |   +-- MTeams.csv
#> |   |   |   \-- MTeamSpellings.csv
#> |   |   +-- MEvents2015.csv
#> |   |   +-- MEvents2016.csv
#> |   |   +-- MEvents2017.csv
#> |   |   +-- MEvents2018.csv
#> |   |   +-- MEvents2019.csv
#> |   |   +-- MPlayers.csv
#> |   |   \-- MSampleSubmissionStage1_2020.csv
#> |   \-- google-cloud-ncaa-march-madness-2020-division-1-mens-tournament.zip
#> +-- large_data
#> \-- output
#>     \-- paris-submission.csv

Download Data

From https://github.com/Kaggle/kaggle-api

kaggle competitions download -c google-cloud-ncaa-march-madness-2020-division-1-mens-tournament -p input
mkdir input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament
unzip input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament.zip -d input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament

More

1. Do the feature engineering on goal and spots with distance(Nandakumar 2020)
2. We ignore the multicollinearity detection in the feature, we choose XGBoost, thus it handles this problem itself, see more https://datascience.stackexchange.com/a/39806/60879.

Code of Conduct

Please note that the ncaa-march-madness-2020 project is released with a Contributor Code of Conduct.
By contributing to this project, you agree to abide by its terms.

License

Apache License (>= 2.0) © Jiaxiang Li;Jiatao Li;Zhipeng Liang;Yue Pan

Reference

Nandakumar, Namita. 2020. “R + Tidyverse in Sports.” RStudio Conference 2020. 2020. https://resources.rstudio.com/rstudio-conf-2020/r-tidyverse-in-sports-namita-nandakumar.