Efficient Weather Prediction Model using Relevant Machine Learning Approach

Authors

  • Prof Abhishek Pandey  Takshshila College of Engineering and Technology, Jabalpur, Madhya Pradesh, India
  • Harsha Khanna  Takshshila College of Engineering and Technology, Jabalpur, Madhya Pradesh, India

Keywords:

Multinomial Linear Regression based classification, judgment, machine learning algorithms, non compensatory, Lasso Regression,Ridge Regression.

Abstract

The alternate in worldwide temperatures, current beyond three years natural disasters, rising sea levels, lowering polar regions may be causing the problem of expertise and predicting these weather phenomena. Prediction is a top importance and that they may be run and simulated as laptop simulations to expect climate variables temperature, precipitation, rainfall and and so forth. Temperature prediction is the most crucial venture for predicting early prediction of rainfall may additionally helps to peasant's in addition to for the people because most people in india may be depends upon the agriculture. This dissertation explains approximately a couple of linear regression approach for the temperature estimation or prediction. It may helps to farmers for taking appropriate decisions on crop yielding. As normally at the equal time there can be a scope to research the prevalence of floods or droughts. The a couple of linear regression evaluation technique applied on the dataset of noaa climate records. The test and our a couple of linear regression method take advantage of the ideal consequences for the temperature than simple linear regression methodology and lasso regression, ridge regression.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-10-30

Issue

Volume 7, Issue 5, September-October-2020

Section

Research Articles

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How to Cite

[1]
Prof Abhishek Pandey, Harsha Khanna "Efficient Weather Prediction Model using Relevant Machine Learning Approach" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 5, pp.124-130, September-October-2020.