PREDICTION OF HUMIDITY IN WEATHER USING LOGISTIC REGRESSION, DECISION TREE, NEAREST NEIGHBOURS, NAIVE BAYESIAN, SUPPORT VECTOR MACHINE AND RANDOM FOREST CLASSIFIERS

Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/908

Title:	PREDICTION OF HUMIDITY IN WEATHER USING LOGISTIC REGRESSION, DECISION TREE, NEAREST NEIGHBOURS, NAIVE BAYESIAN, SUPPORT VECTOR MACHINE AND RANDOM FOREST CLASSIFIERS
Authors:	Sujatha, G Someswara Rao, Dr Chinta Srinivasa Rao, T
Keywords:	Engineering and Technology Prediction of Humidity Weather Logistic Regression Decision Tree Naive Bayesian Support Vector Machine Randon Forst Classifiers
Issue Date:	20-Sep-2019
Publisher:	Asian Research Publishing Network
Abstract:	The ultimate objective of this system is to predict the variation of humidity in the weather over a given period. The weather condition at any instance is described by using different kinds of variables. Out of these variables, significant variables only are used in the weather prediction process. The selection of such variables depends strongly on the location. The existing weather condition parameters are used to fit a model and by using the machine learning techniques and extrapolating the information, the future variations in the parameters are analyzed.
URI:	http://142.54.178.187:9060/xmlui/handle/123456789/908
ISSN:	1819-6608
Appears in Collections:	Journals

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