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Prediction of humidity in weather using logistic regression, decision tree, nearest neighbours, naive bayesian, support vector machine and random forest classifiers

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Title: Prediction of humidity in weather using logistic regression, decision tree, nearest neighbours, naive bayesian, support vector machine and random forest classifiers

Authors: G. Sujatha, Chinta Someswara Rao, T. Srinivasa Rao

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2019

Volume: 14

Issue: 18

Language: English

Categories
Classification (77) Computer Science (14978) Weather Forecasting (28) Machine Learning (3478) Logistic Regression (12)

Abstract

The ultimate objective of this system is to predicting 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.

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