This is a dataset that consists of a cleaned up subset of peer-to-peer loan data published by LendingClub. We have built upon the results processed by the open-source preprocess_lending_club_data repository, which have been CC0-licensed on Kaggle here. We processed the data into a smaller dataset format containing fewer rows and columns in order to more accurately reflect the real-world use-case of learning creditworthiness models from historical lending data.

The p2p_loans_470k dataset is intended to reflect a more real-world use case for machine learning than many typical academic datasets. The training set contains over 300k rows detailing the outcome of real loans issued between June 2012 through March 2015, while the test-set contains loans issued from April 2015 through August 2015.

It should be noted that this dataset is not a perfect reflection of a real-world creditworthiness modeling use case. In a real use-case, models trained on fully-resolved historical lending data would be applied to loans issued years after the conclusion of the training set. In this data set, on the other hand, the test set is contiguous to the training set. Nonetheless, the dataset spans years of real-world business practice, likely violating many traditional statistical assumptions of time-series stationarity.

The full dataset is already present as part of this repo. Simply copy the p2p_loans_470k directory to any place you want the dataset.

If you want to rebuild the dataset yourself, you will need to have python3 and cmake installed. You will also need to install a Kaggle API token (see https://github.com/Kaggle/kaggle-api#api-credentials for details). In the case that a new version of the base dataset is uploaded to Kaggle, it will be necessary to manually download the old version of the dataset from Kaggle, as at the time of writing the Kaggle API does not appear to support downloading previous versions of datasets.

Once you have completed this setup, simply run the default cmake command to install the proper dependencies, download the raw data, and run the processing script:

make

Note: If you want to use a virtualenvironment, create and activate your environment before running make.

Alternatively, if you have git-lfs installed, you will have access to the full raw dataset just by cloning this repository. The standard bulid procedure does not require this dataset to be downloaded, but if you do have git-lfs installed and thus pull the raw dataset as part of cloning this repository, you can alternatively run the following shorter make sequence to avoid the step of downloading the raw data from Kaggle:

make install_requirements process_data

You can see a demo Jupyter notebook that shows step by step how to use this dataset for creditworthiness modeling here.