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Reading_Hydro_upstream
ARFF
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Reading_Hydro_upstream
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ARFF
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Visibility: public
Uploaded 30-09-2022 by
Laurens Krudde
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Machine Learning
Meteorology
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Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK.
2 features
downstream
(target)
numeric
55 unique values
0 missing
timestamp
numeric
1000 unique values
0 missing
Show all 2 features
19 properties
NumberOfInstances
1000
Number of instances (rows) of the dataset.
NumberOfFeatures
2
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
Show all 19 properties
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