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

dc.contributor.author	Sujatha, G
dc.contributor.author	Someswara Rao, Dr Chinta
dc.contributor.author	Srinivasa Rao, T
dc.date.accessioned	2019-11-05T09:41:31Z
dc.date.available	2019-11-05T09:41:31Z
dc.date.issued	2019-09-20
dc.identifier.issn	1819-6608
dc.identifier.uri	http://142.54.178.187:9060/xmlui/handle/123456789/908
dc.description.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.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Asian Research Publishing Network	en_US
dc.subject	Engineering and Technology	en_US
dc.subject	Prediction of Humidity	en_US
dc.subject	Weather	en_US
dc.subject	Logistic Regression	en_US
dc.subject	Decision Tree	en_US
dc.subject	Naive Bayesian	en_US
dc.subject	Support Vector Machine	en_US
dc.subject	Randon Forst Classifiers	en_US
dc.title	PREDICTION OF HUMIDITY IN WEATHER USING LOGISTIC REGRESSION, DECISION TREE, NEAREST NEIGHBOURS, NAIVE BAYESIAN, SUPPORT VECTOR MACHINE AND RANDOM FOREST CLASSIFIERS	en_US
dc.type	Article	en_US