Datasets:
Add banner, live demo badge, fix DatasetGenerationCastError with configs
Browse files
README.md
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pretty_name: "LendingClub Loan Data (2007-2018)"
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size_categories:
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- 1M<n<10M
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---
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# LendingClub Loan Data (2007-2018)
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[](https://creativecommons.org/publicdomain/zero/1.0/) [](https://builderslab.dev/) [](https://www.linkedin.com/company/builderslabdev) [](https://github.com/BuildersLab/Credit-Risk-Default)
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Personal loan applications and originations from LendingClub (2007-2018), sourced from Kaggle. Used by [BuildersLab](https://github.com/BuildersLab)'s **Credit Risk Default** project to build an explainable model that predicts **loan application defaults**, not a credit card product, for a fictional bank case study.
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- **Project repo:** https://github.com/BuildersLab/Credit-Risk-Default
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- **Source:** [Kaggle: All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club) (`wordsforthewise/lending-club`)
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- **License:** CC0 1.0 Universal (Public Domain)
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## Data Dictionary
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Two project-generated summary files under `data_dictionary/`, produced while cleaning
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the raw LendingClub CSV (see [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/main/notebooks/00_cleaning.ipynb)), not Kaggle's raw column list.
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- **`data_dictionary_summary.csv`**: one row per column: `Variable`, `Description`
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(LendingClub's official column definition), `Missing Values` (count),
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pretty_name: "LendingClub Loan Data (2007-2018)"
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size_categories:
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- 1M<n<10M
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configs:
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- config_name: data_dictionary_summary
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data_files: data_dictionary/data_dictionary_summary.csv
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- config_name: missing_values_summary
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data_files: data_dictionary/missing_values_summary.csv
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---
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# LendingClub Loan Data (2007-2018)
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[](https://creativecommons.org/publicdomain/zero/1.0/) [](https://builderslab.dev/) [](https://www.linkedin.com/company/builderslabdev) [](https://github.com/BuildersLab/Credit-Risk-Default) [](https://portfolio-risk-prediction.streamlit.app/)
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Personal loan applications and originations from LendingClub (2007-2018), sourced from Kaggle. Used by [BuildersLab](https://github.com/BuildersLab)'s **Credit Risk Default** project to build an explainable model that predicts **loan application defaults**, not a credit card product, for a fictional bank case study.
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- **Project repo:** https://github.com/BuildersLab/Credit-Risk-Default
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- **Live demo:** https://portfolio-risk-prediction.streamlit.app/
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- **Source:** [Kaggle: All Lending Club loan data](https://www.kaggle.com/datasets/wordsforthewise/lending-club) (`wordsforthewise/lending-club`)
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- **License:** CC0 1.0 Universal (Public Domain)
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## Data Dictionary
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Two project-generated summary files under `data_dictionary/`, produced while cleaning
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the raw LendingClub CSV (see [`notebooks/00_cleaning.ipynb`](https://github.com/BuildersLab/Credit-Risk-Default/blob/main/notebooks/00_cleaning.ipynb)), not Kaggle's raw column list. They have different columns, so each is declared as its own config above (see `configs:` in the metadata) rather than being auto-merged.
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- **`data_dictionary_summary.csv`**: one row per column: `Variable`, `Description`
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(LendingClub's official column definition), `Missing Values` (count),
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