Data
Forest-Fire-Area

Forest-Fire-Area

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Content The dataset contains 517 fires from the Montesinho natural park in Portugal. For each incident weekday, month, coordinates, and the burnt area are recorded, as well as several meteorological data such as rain, temperature, humidity, and wind. The workflow reads the data and trains a regression model based on the spatial, temporal, and weather variables. Acknowledgements All credit for this dataset goes to P. Cortez and A. Morais. P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9. Burning Area Prediction

13 features

area (target)numeric251 unique values
0 missing
Xnumeric9 unique values
0 missing
Ynumeric7 unique values
0 missing
monthstring12 unique values
0 missing
daystring7 unique values
0 missing
FFMCnumeric106 unique values
0 missing
DMCnumeric215 unique values
0 missing
DCnumeric219 unique values
0 missing
ISInumeric119 unique values
0 missing
tempnumeric192 unique values
0 missing
RHnumeric75 unique values
0 missing
windnumeric21 unique values
0 missing
rainnumeric7 unique values
0 missing

19 properties

517
Number of instances (rows) of the dataset.
13
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.
11
Number of numeric attributes.
0
Number of nominal attributes.
0.03
Number of attributes divided by the number of instances.
84.62
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.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-13.81
Average class difference between consecutive instances.
0
Percentage of missing values.

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