load_loans(roles=False, units=False, as_pandas=False)¶
Binary classification dataset on loan default
The loans dataset is based on financial dataset from the the CTU Prague Relational Learning Repository.
The original publication is: Berka, Petr (1999). Workshop notes on Discovery Challange PKDD’99.
The dataset contains information on 606 successful and 76 unsuccessful loans. After some preprocessing it contains 4 tables
population: Information about the loans themselves, such as the date of creation, the amount, and the planned duration of the loan. The target variable is the status of the loan (default/no default)
order: Information about permanent orders, debited payments and account balances.
trans: Information about transactions and accounts balances.
meta: Meta information about the obligor, such as gender and geo-information
The population table is split into a training and a testing set at 80% of the main population.
Return data as pandas.DataFrame s
Return data with roles set
Return data with units set
>>> df_getml = getml.datasets.load_loans() >>> type(df_getml["population_train"]) ... getml.data.data_frame.DataFrame
For an full analysis of the loans dataset including all necessary preprocessing steps please refer to getml-examples.