Data
airfoil_self_noise

airfoil_self_noise

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Citation Please refer to UCI's [citation policy](https://archive.ics.uci.edu/ml/citation_policy.html). Source Donor: Dr Roberto Lopez robertolopez '@' intelnics.com Intelnics Creators: Thomas F. Brooks, D. Stuart Pope and Michael A. Marcolini NASA Note that the names of the columns have been altered to be more descriptive. Data Description The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments. Attribute description This problem has the following inputs: * frequency - Frequency, in Hertzs. * angle - Angle of attack, in degrees. * length - Chord length, in meters. * velocity - Free-stream velocity, in meters per second. * thickness - Suction side displacement thickness, in meters. The only output is: * pressure - Scaled sound pressure level, in decibels. Relevant Papers: T.F. Brooks, D.S. Pope, and A.M. Marcolini. Airfoil self-noise and prediction. Technical report, NASA RP-1218, July 1989. K. Lau. A neural networks approach for aerofoil noise prediction. Master thesis, Department of Aeronautics. Imperial College of Science, Technology and Medicine (London, United Kingdom), 2006. R. Lopez. Neural Networks for Variational Problems in Engineering. PhD Thesis, Technical University of Catalonia, 2008.

6 features

pressure (target)numeric1456 unique values
0 missing
frequencynumeric21 unique values
0 missing
anglenumeric27 unique values
0 missing
lengthnumeric6 unique values
0 missing
velocitynumeric4 unique values
0 missing
thicknessnumeric105 unique values
0 missing

19 properties

1503
Number of instances (rows) of the dataset.
6
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.
6
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-1.52
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage 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.
0
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: pressure
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