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  Published Paper Details:

  Paper Title

Weather Prediction Using Machine Learning

  Authors

  Prof. Anjali Kadam,  Shraddha Idhate,  Gauri Sonawane,  Rucha Sathe,  Poonam Gundale

  Keywords

: Weather Forecasting, Weather prediction, machine learning, SVM, ANN, Naive Bayes.

  Abstract


To predict the weather of a particular place at a specific time is known as Weather Forecasting. It is an application of science and technology. Weather forecast is more helpful for people as it predicts the possibilities of changes in weather conditions. The weather forecast can be done in many ways, collecting data and analysing it, image processing, by using methodologies like Normalization, Clustering, and cloud mask algorithms, etc. This paper is based on the process of Weather Forecasting. This system will be using Machine learning algorithms like Support Vector Machine(SVM), Time series based recurrent neural network, Random Forest, Naive Bayes, Artificial Neural Network, and Decision Tree. This system will accept data on current weather conditions such as current temperature, wind conditions, and humidity. It will predict the weather conditions of a certain location on a given day and time. This system will be useful in Air Traffic, Marine, Agriculture, Forestry, Military, and Navy, etc.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT23A5325

  Paper ID - 238489

  Page Number(s) - l99-l102

  Pubished in - Volume 11 | Issue 5 | May 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Prof. Anjali Kadam,  Shraddha Idhate,  Gauri Sonawane,  Rucha Sathe,  Poonam Gundale,   "Weather Prediction Using Machine Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 5, pp.l99-l102, May 2023, Available at :http://www.ijcrt.org/papers/IJCRT23A5325.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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