In today’s modern world, the environmental wealth is degraded due to the advancement in the technology. The software development leads to the emission of electronic waste that affects the whole part of the country. The forest area and the agricultural land are converted into living places, companies, industries, server warehouse and heavy workstations. Due to this, heavy damages occur in the environmental resources. The basic characteristics of the nature quality are becoming poor due to the technological advancement. Due to the heavy emission of rays in the environment, there is a chance for the occurrence of fire in the forest. This leads to the challenging issue of predicting the area coverage of the fire in the forest. After the event of fire damage, it is a difficult task to analyze the area that suffered from the fire. With this analytical view, this paper focuses on finding the area coverage of fire using various regression algorithms. The forest area coverage dataset from the UCI machine learning repository is used for analyzing the area coverage of the fire. The prediction of area coverage of fire is accomplished in four ways. Firstly, the raw data set is fitted with various regression algorithms to predict the fire area coverage. Secondly, the data set is tailored by the feature selection algorithm namely backward elimination technique. Thirdly, the backward eliminated reduced fire area coverage data set is fitted with various regression algorithms to predict the fire area coverage. Fourth, the performance analysis is done for the raw data set and backward eliminated reduced fire area coverage data set by reviewing the performance metrics mean squared error (MSE), Mean Absolute Error (MAE) and R2 Score. This paper is implemented by python scripts in Anaconda Spyder Navigator. Experimental Result shows that the Passive Aggressive regressor have the effective prediction of fire area coverage with minimum MSE of 0.07, MAE of 1.03 and equitable R2 Score of 0.93 without backward elimination. In the same way, the Passive Aggressive regressor MSE of 0.06, MAE of 1.02 and equitable R2 Score of 0.96 with backward elimination

In recent times, the natural resources are demolished due to the technological growth. The agricultural and the forest area are transformed to industries, Storage Warehouse and Container logistics companies to facilitate the living standards. This leads to scarcity of natural resources for the people to live a comfortable life. Due to the change of natural environment and fluctuations in the climate conditions, the forest has the chance of occurrence of fire. The forest fire is the resultant of high temperature, land mine, flight crashes and satellite damages from the environment. The precaution must be taken in advance to protect the coverage of fire. The less attention to fire control may lead to entire damage of the forest and the spreading of fire occurs due to the high wind blow. This makes researchers to focus on helping the forest area to overcome from the fire attack. The detection of fire type is a challenging task after the occurrence of the damage. With this view, we address the prediction of fire type classification using machine learning classification algorithms. The Forest Cover Type dataset is downloaded from UCI Data warehouse repository and done with classification analysis. The prediction of absent hours is achieved in the methodology of four steps. At first, the important feature attributes are found and depicted as a chart. Secondly, the raw dataset is applied to all the classification models like Logistic regression, Kernel SVM, KNN, Decision Tree, Naïve Bayes and Random Forest. Thirdly, the dataset is reduced with PCA and then the reduced dataset is fitted to all the classifiers. Fourth, Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Recall and Precision. The real time execution is performed by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Random Forest classifier is obtained with the accuracy of 92% before applying PCA. After applying PCA, the classifier namely Random forest is analyzed to be having the accuracy of 78% for 15 components, 83% for 20 components and 89% for 25 components.