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Fire Forest data set - Regression

Updated: Nov 3, 2021

Description :

This dataset contains information about forest fires. This dataset is used to Predict Forest Fires using Meteorological Data. In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transform. Four different input setups were used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority.

Recommended Model :

Algorithms to be used, regression, random forest, Support Vector Machines etc.

Recommended Projects :

To Predict the burned area of forest fires by using this dataset.

Dataset link

Overview of data

Detailed overview of dataset

  • Records in the dataset = 517 ROWS

  • Columns in the dataset = 13 COLUMNS

  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).



import pandas as pd
# Load Data
file_loc = "data\\forestfires.csv"
forest_fire_data = pd.read_csv(file_loc)

Total Number of Rows and columns in the dataset

# Number of Rows and columns 
rows_col = forest_fire_data.shape
print("Total number of Rows in the dataset : {}".format(rows_col[0]))
print("Total number of columns in the dataset : {}".format(rows_col[1]))

Check Details

# Data information

Check missing values in the dataset

# Missing Values

Statistical information

# Statistical information

Data Visualization

import seaborn as sns
import matplotlib.pyplot as plt
# correlation
corr = forest_fire_data.corr()'coolwarm')

Count plot of the month

sns.countplot(x= "month",data=forest_fire_data)

Count plot of Day

sns.countplot(x= "day",data=forest_fire_data)

Histogram plot of rain.

# Histogram 

Histogram plot of FFMC

# Histogram 

Histogram plot of DMC

# Histogram 

Histogram plot of Tempreture

# Histogram 

Other related data

If you need implementation for any of the topics mentioned above or assignment help on any of its variants, feel free to contact us.

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