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LoanDefaultPrediction

LoanDefaultPrediction

active ARFF Publicly available Visibility: public Uploaded 11-01-2023 by Frank Wallace
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical features" benchmark. Original link: https://openml.org/d/6331 Original description: Data from training set of the Kaggle Loan Default Prediction - Imperial College London challenge: https://www.kaggle.com/c/loan-default-prediction This data corresponds to a set of financial transactions associated with individuals. The data has been standardized, de-trended, and anonymized. You are provided with over two hundred thousand observations and nearly 800 features. Each observation is independent from the previous. For each observation, it was recorded whether a default was triggered. In case of a default, the loss was measured. This quantity lies between 0 and 100. It has been normalised, considering that the notional of each transaction at inception is 100. For example, a loss of 60 means that only 40 is reimbursed. If the loan did not default, the loss was 0. You are asked to predict the losses for each observation in the test set. Missing feature values have been kept as is, so that the competing teams can really use the maximum data available, implementing a strategy to fill the gaps if desired. Note that some variables may be categorical (e.g. f776 and f777). The competition sponsor has worked to remove time-dimensionality from the data. However, the observations are still listed in order from old to new.

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f646numeric55238 unique values
0 missing
f647numeric55056 unique values
0 missing
f648numeric1803 unique values
0 missing
f649numeric3102 unique values
0 missing
f650numeric790 unique values
0 missing
f651numeric1633 unique values
0 missing
f652numeric617 unique values
0 missing
f653numeric151 unique values
0 missing
f654numeric11715 unique values
0 missing
f655numeric11702 unique values
0 missing
f656numeric3140 unique values
0 missing
f657numeric50007 unique values
0 missing
f658numeric55252 unique values
0 missing
f659numeric52665 unique values
0 missing
f660numeric286 unique values
0 missing
f661numeric191 unique values
0 missing
f662numeric9272 unique values
0 missing
f663numeric8824 unique values
0 missing
f664numeric20096 unique values
0 missing
f665numeric6388 unique values
0 missing
f666numeric163 unique values
0 missing
f667numeric47294 unique values
0 missing
f668numeric52347 unique values
0 missing
f669numeric28555 unique values
0 missing
f670numeric1665 unique values
0 missing
f671numeric4754 unique values
0 missing
f672numeric1839 unique values
0 missing
f673numeric2313 unique values
0 missing
f674numeric150 unique values
0 missing
f675numeric33 unique values
0 missing
f676numeric94 unique values
0 missing
f677numeric34 unique values
0 missing
f680numeric13919 unique values
0 missing
f681numeric166 unique values
0 missing
f682numeric8596 unique values
0 missing
f683numeric20137 unique values
0 missing
f684numeric50890 unique values
0 missing
f685numeric20082 unique values
0 missing
f686numeric50894 unique values
0 missing
f687numeric53049 unique values
0 missing
f688numeric49551 unique values
0 missing
f689numeric53133 unique values
0 missing
f690numeric49260 unique values
0 missing
f691numeric53136 unique values
0 missing
f692numeric53146 unique values
0 missing
f693numeric6140 unique values
0 missing
f694numeric43514 unique values
0 missing
f695numeric54620 unique values
0 missing
f696numeric44580 unique values
0 missing
f697numeric44829 unique values
0 missing
f698numeric54164 unique values
0 missing
f699numeric49739 unique values
0 missing
f703numeric48114 unique values
0 missing
f704numeric47170 unique values
0 missing
f705numeric45754 unique values
0 missing
f706numeric48744 unique values
0 missing
f707numeric48486 unique values
0 missing
f708numeric47481 unique values
0 missing
f709numeric46349 unique values
0 missing
f710numeric48992 unique values
0 missing
f711numeric48604 unique values
0 missing
f712numeric49562 unique values
0 missing
f713numeric166 unique values
0 missing
f714numeric49517 unique values
0 missing
f715numeric55319 unique values
0 missing
f716numeric53155 unique values
0 missing
f717numeric55319 unique values
0 missing
f718numeric52684 unique values
0 missing
f719numeric52602 unique values
0 missing
f720numeric52505 unique values
0 missing
f721numeric52379 unique values
0 missing
f722numeric166 unique values
0 missing
f723numeric265 unique values
0 missing
f724numeric117 unique values
0 missing
f726numeric52415 unique values
0 missing
f727numeric3401 unique values
0 missing
f728numeric9658 unique values
0 missing
f729numeric166 unique values
0 missing
f730numeric52101 unique values
0 missing
f731numeric54590 unique values
0 missing
f732numeric54612 unique values
0 missing
f733numeric1198 unique values
0 missing
f734numeric3762 unique values
0 missing
f735numeric55318 unique values
0 missing
f737numeric4788 unique values
0 missing
f738numeric20537 unique values
0 missing
f739numeric31625 unique values
0 missing
f740numeric16024 unique values
0 missing
f741numeric166 unique values
0 missing
f742numeric45585 unique values
0 missing
f743numeric52229 unique values
0 missing
f744numeric53032 unique values
0 missing
f745numeric37198 unique values
0 missing
f746numeric19046 unique values
0 missing
f747numeric44256 unique values
0 missing
f748numeric48312 unique values
0 missing
f749numeric47455 unique values
0 missing
f750numeric46633 unique values
0 missing
f751numeric46989 unique values
0 missing
f752numeric44936 unique values
0 missing
f753numeric45742 unique values
0 missing
f754numeric41175 unique values
0 missing
f755numeric35731 unique values
0 missing
f756numeric46349 unique values
0 missing
f757numeric48000 unique values
0 missing
f758numeric47100 unique values
0 missing
f759numeric46116 unique values
0 missing
f760numeric47050 unique values
0 missing
f761numeric49254 unique values
0 missing
f762numeric49124 unique values
0 missing
f763numeric1557 unique values
0 missing
f765numeric94 unique values
0 missing
f766numeric180 unique values
0 missing
f767numeric823 unique values
0 missing
f768numeric4652 unique values
0 missing
f769numeric4922 unique values
0 missing
f770numeric165 unique values
0 missing
f771numeric3611 unique values
0 missing
f772numeric2772 unique values
0 missing
f773numeric2208 unique values
0 missing
f774numeric3047 unique values
0 missing
f775numeric17970 unique values
0 missing
f776nominal2 unique values
0 missing
f777nominal2 unique values
0 missing

19 properties

55319
Number of instances (rows) of the dataset.
736
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
732
Number of numeric attributes.
4
Number of nominal attributes.
0.54
Percentage of nominal attributes.
-0.64
Average class difference between consecutive instances.
99.46
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
0.27
Percentage of binary attributes.
2
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0.01
Number of attributes divided by the number of instances.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: loss
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