Fire Forest data set - Regression

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.

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

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

Data set Link : UCI MLR - https://archive.ics.uci.edu/ml/datasets/forest+fires

Kaggle : - https://www.kaggle.com/elikplim/forest-fires-data-set

Detailed overview of dataset

Dataset

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

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
 forest_fire_data.info()

Check missing values in the dataset

# Missing Values
 forest_fire_data.isna().sum()

Statistical information

# Statistical information
 forest_fire_data.describe()

Data Visualization

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

Count plot of the month

sns.set_style("whitegrid")
 plt.figure(figsize=(8,5))
 sns.countplot(x= "month",data=forest_fire_data)

Count plot of Day

sns.set_style("whitegrid")
 plt.figure(figsize=(8,5))
 sns.countplot(x= "day",data=forest_fire_data)

Histogram plot of rain.

# Histogram
 plt.figure(figsize=(8,5))
 sns.histplot(x="rain",data=forest_fire_data)
 

Histogram plot of FFMC

# Histogram
 plt.figure(figsize=(8,5))
 sns.histplot(x="FFMC",data=forest_fire_data)

Histogram plot of DMC

# Histogram
 plt.figure(figsize=(8,5))
 sns.histplot(x="DMC",data=forest_fire_data)

Histogram plot of Tempreture

# Histogram
 plt.figure(figsize=(8,5))
 sns.histplot(x="temp",data=forest_fire_data)

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