Data
LoanDefaultPrediction

LoanDefaultPrediction

active ARFF Publicly available Visibility: public Uploaded 21-06-2022 by Leo Grin
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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 categorical and numerical features" benchmark. 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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f491numeric37702 unique values
0 missing
f757numeric48000 unique values
0 missing
f223numeric36 unique values
0 missing
f492numeric20385 unique values
0 missing
f758numeric47100 unique values
0 missing
f224numeric1425 unique values
0 missing
f493numeric13417 unique values
0 missing
f759numeric46116 unique values
0 missing
f225numeric1425 unique values
0 missing
f494numeric3896 unique values
0 missing
f760numeric47050 unique values
0 missing
f226numeric1425 unique values
0 missing
f495numeric22283 unique values
0 missing
f761numeric49254 unique values
0 missing
f227numeric1425 unique values
0 missing
f496numeric49216 unique values
0 missing
f762numeric49124 unique values
0 missing
f228numeric42 unique values
0 missing
f497numeric53848 unique values
0 missing
f763numeric1557 unique values
0 missing
f229numeric134 unique values
0 missing
f498numeric9240 unique values
0 missing
f765numeric94 unique values
0 missing
f230numeric203 unique values
0 missing
f499numeric305 unique values
0 missing
f766numeric180 unique values
0 missing
f231numeric94 unique values
0 missing
f500numeric41047 unique values
0 missing
f767numeric823 unique values
0 missing
f233numeric31 unique values
0 missing
f501numeric28872 unique values
0 missing
f768numeric4652 unique values
0 missing
f234numeric1325 unique values
0 missing
f502numeric24593 unique values
0 missing
f769numeric4922 unique values
0 missing
f235numeric1325 unique values
0 missing
f503numeric12520 unique values
0 missing
f770numeric165 unique values
0 missing
f236numeric1325 unique values
0 missing
f504numeric3428 unique values
0 missing
f771numeric3611 unique values
0 missing
f237numeric1325 unique values
0 missing
f505numeric21559 unique values
0 missing
f772numeric2772 unique values
0 missing
f238numeric41 unique values
0 missing
f506numeric49305 unique values
0 missing
f773numeric2208 unique values
0 missing
f239numeric121 unique values
0 missing
f507numeric53843 unique values
0 missing
f774numeric3047 unique values
0 missing
f240numeric180 unique values
0 missing
f508numeric9240 unique values
0 missing
f775numeric17970 unique values
0 missing
f241numeric92 unique values
0 missing
f509numeric310 unique values
0 missing
f776nominal2 unique values
0 missing
f243numeric25 unique values
0 missing
f510numeric40916 unique values
0 missing
f777nominal2 unique values
0 missing
f244numeric1213 unique values
0 missing
f511numeric28567 unique values
0 missing
f245numeric1213 unique values
0 missing
f512numeric22943 unique values
0 missing
f246numeric1213 unique values
0 missing
f513numeric5566 unique values
0 missing
f247numeric1213 unique values
0 missing
f514numeric27864 unique values
0 missing
f248numeric35 unique values
0 missing
f515numeric894 unique values
0 missing
f249numeric93 unique values
0 missing
f516numeric1472 unique values
0 missing
f250numeric132 unique values
0 missing
f517numeric6200 unique values
0 missing
f251numeric100 unique values
0 missing
f518numeric14568 unique values
0 missing
f253numeric56 unique values
0 missing
f519numeric54802 unique values
0 missing
f254numeric2573 unique values
0 missing
f520numeric55242 unique values
0 missing
f255numeric2573 unique values
0 missing
f521numeric8855 unique values
0 missing
f256numeric2573 unique values
0 missing
f522numeric7079 unique values
0 missing
f257numeric2573 unique values
0 missing
f523numeric55312 unique values
0 missing
f258numeric38 unique values
0 missing
f524numeric23155 unique values
0 missing
f259numeric129 unique values
0 missing
f525numeric7202 unique values
0 missing
f260numeric141 unique values
0 missing
f526numeric55317 unique values
0 missing
f261numeric100 unique values
0 missing
f527numeric55129 unique values
0 missing
f263numeric70 unique values
0 missing
f528numeric51667 unique values
0 missing
f264numeric3620 unique values
0 missing
f529numeric55308 unique values
0 missing
f265numeric3620 unique values
0 missing
f530numeric54792 unique values
0 missing
f266numeric3620 unique values
0 missing
f531numeric8281 unique values
0 missing
f267numeric3620 unique values
0 missing
f532numeric9464 unique values
0 missing
f268numeric33 unique values
0 missing
f533numeric259 unique values
0 missing
f269numeric196 unique values
0 missing
f534numeric47891 unique values
0 missing
f270numeric104 unique values
0 missing
f535numeric55309 unique values
0 missing
f271numeric42873 unique values
0 missing
f536numeric73 unique values
0 missing
f272numeric28789 unique values
0 missing
f537numeric55151 unique values
0 missing
f273numeric45656 unique values
0 missing
f538numeric13935 unique values
0 missing
f274numeric31840 unique values
0 missing
f539numeric4617 unique values
0 missing
f275numeric23540 unique values
0 missing
f540numeric21265 unique values
0 missing
f276numeric3234 unique values
0 missing
f541numeric50288 unique values
0 missing
f277numeric19347 unique values
0 missing
f542numeric54488 unique values
0 missing
f278numeric52279 unique values
0 missing
f543numeric9464 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.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.
0.27
Percentage of binary attributes.
0
Percentage of instances having missing values.
-0.64
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
99.46
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0.54
Percentage of nominal attributes.

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