Data
delays_zurich_transport

delays_zurich_transport

active ARFF CCZero Visibility: public Uploaded 11-01-2023 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 both numerical and categorical features" benchmark. Original link: https://openml.org/d/40753 Original description: Zurich public transport delay data 2016-10-30 03:30:00 CET - 2016-11-27 01:20:00 CET cleaned and prepared at Open Data Day 2017.

12 features

delay (target)numeric4082 unique values
0 missing
vehicle_typenominal3 unique values
0 missing
directionnominal2 unique values
0 missing
weekdaynominal7 unique values
0 missing
tempnumeric143 unique values
0 missing
windspeed_maxnumeric131 unique values
0 missing
windspeed_avgnumeric66 unique values
0 missing
precipitationnumeric15 unique values
0 missing
dew_pointnumeric120 unique values
0 missing
humiditynumeric46 unique values
0 missing
hournumeric23 unique values
0 missing
dayminutenumeric132 unique values
0 missing

19 properties

5465575
Number of instances (rows) of the dataset.
12
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.
9
Number of numeric attributes.
3
Number of nominal attributes.
8.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.14
Average class difference between consecutive instances.
75
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
25
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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.
1
Number of binary attributes.

1 tasks

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