UCI: Forest fires

This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data. This data set is sourced from the UCI Machine Learning Repository.

Attribute information:

  1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9
  2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9
  3. month - month of the year: "jan" to "dec"
  4. day - day of the week: "mon" to "sun"
  5. FFMC - FFMC index from the FWI system: 18.7 to 96.20
  6. DMC - DMC index from the FWI system: 1.1 to 291.3
  7. DC - DC index from the FWI system: 7.9 to 860.6
  8. ISI - ISI index from the FWI system: 0.0 to 56.10
  9. temp - temperature in Celsius degrees: 2.2 to 33.30
  10. RH - relative humidity in %: 15.0 to 100
  11. wind - wind speed in km/h: 0.40 to 9.40
  12. rain - outside rain in mm/m2 : 0.0 to 6.4
  13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform).
Sep 25, 2017
Sep 25, 2017
Environment
IBM
XYmonthdayFFMCDMCDCISItempRHwindrainarea
XYmonthdayFFMCDMCDCISItempRHwindrainarea
75marfri86.226.294.35.18.2516.700
74octtue90.635.4669.16.718330.900
74octsat90.643.7686.96.714.6331.300
86marfri91.733.377.598.39740.20
86marsun89.351.3102.29.611.4991.800
86augsun92.385.348814.722.2295.400
86augmon92.388.9495.68.524.1273.100
86augmon91.5145.4608.210.78862.200
86septue91129.5692.6713.1635.400
75sepsat92.588698.67.122.840400
75sepsat92.588698.67.117.8517.200
75sepsat92.873.271322.619.338400
65augfri63.570.8665.30.817726.700
65sepmon90.9126.5686.5721.3422.200
65sepwed92.9133.3699.69.226.4214.500
Drop file to add data source.