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Weather Forecasting using Linear Regression In Machine Learning

EasyChair Preprint no. 3565

9 pagesDate: June 7, 2020


Prediction requires accurate classification of data .In order to predict the uncertain things, we need to analyse various factors which involved either directly or indirectly. Weather is one of the most influential environmental constraints in every phase of our lives on the earth. So as to make everyday tasks we are very much rely on weather and need to know weather condition on before hands. This could be achieved by predicting the weather condition such as humidity, rainfall, temperature, thunder, fog, etc. This helps us in protecting ourselves from abnormal conditions and avoids unnecessary delays. The main objective of this paper is to design an effective weather prediction model by the use of multivariate regression or multiple linear regressions and support vector machine (SVM). As of now, there are various debates going on around the world either scientifically or non-scientifically regarding the change of Earth's climate in fore coming decades/centuries and what impact it will cause on all the living creatures. Scientific models which predict future climates offer the best plan or aspiration for providing the information which will allow the world's policy maker to take preventive measures and make better decisions for the future of the Earth and for the future lives. This paper explores about weather forecast in effective way.

Keyphrases: Coefficient Correlation, data preprocessing, linear regression

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Om Narayan Jaiswal},
  title = {Weather Forecasting using Linear Regression In Machine Learning},
  howpublished = {EasyChair Preprint no. 3565},

  year = {EasyChair, 2020}}
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