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, with the technological advancement the industry and organization are transforming all their inflow and outflow operations into digital identity. At the outset, the name of the organization is also in the hands of the employee. One of the major needs of the employee in the working environment is to avail leave or vacation based on their family circumstances. Based on the health condition and need of the employee, the organization must extend their leave for the satisfaction of the employee. The performance of the employee is also predicted based on the working days in the organization. With this view, this paper attempts to analyze the performance of the employee and the number of working hours by using machine learning algorithms. The Absenteeism at work dataset from UCI machine learning Repository is used for prediction analysis. The prediction of absent hours is achieved in three ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly fitted to the regression models like Linear regression, SRD regression, RANSAC regression, Ridge regression, Huber regression, ARD Regression, Passive Aggressive Regression and Theilson Regression. Thirdly, the Performance analysis is done by analyzing the performance metrics like Mean Squared Error, Mean Absolute Error, R2 Score, Explained Variance Score and Mean Squared Log Error. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Passive Aggressive Regression have achieved the effective prediction of number of absent hours with minimum MSE of 0.04, MAE of 0.16, EVS of 0.03, MSLE of 0.32 and reasonable R2 Score of 0.89.

Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

With the vast growth of technology, the world is moving towards different style of instant food habits which lead to the irregular functioning of the body organs. One such victim problem we face is the existence of hypothyroid in the body. Hypothyroid is the under active thyroid circumstance, where the thyroid gland does not produce required amount of essential hormones. The prediction of hypothyroid still remains as a challenging task due to the non availability of exact symptoms. By keeping this analysis in mind, this paper focus on prediction of hypothyroid based on the clinical parameters. The hypothyroid dataset from the UCI machine learning repository is used for predicting the existence of hypothyroid using machine learning classification algorithms. The prediction of existence of hypothyroid is carried out in four ways. Firstly, the raw data set is fitted with various classification algorithms to find the existence of hypothyroid. Secondly, the data set is tailored by the Ada Boost Regressor algorithm to extract the important features from the hypothyroid dataset. Then the extracted feature importance of the hypothyroid dataset is then fitted to the various classification algorithms. Thirdly, the hypothyroid dataset is subjected to the dimensionality reduction using principal component analysis. The PCA reduced hypothyroid dataset is then fitted with classification algorithms to predict the existence of hypothyroid. Fourth, the performance analysis is done for the raw data set, Feature importance AdaBoost hypothyroid dataset and PCA reduced hypothyroid dataset by comparing the performance metrics like precision, recall, FScore and Accuracy. This paper is implemented by python scripts in Anaconda Spyder Navigator. Experimental Result shows that the Random Forest, Naive Bayes and Logistic regression have the accuracy of 99.5 for the raw dataset, feature importance reduced dataset and the accuracy of 99.8 for the five component reduced PCA dataset.