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IJSTR >> Volume 9 - Issue 6, June 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Estimating Rainfall Prediction using Machine Learning Techniques on a Dataset

[Full Text]

 

AUTHOR(S)

R Vijayan, V Mareeswari, P Mohankumar, G Gunasekaran K Srikar

 

KEYWORDS

Classification, Data Analysis, Decision Tree, Feature Extraction, Machine Learning, Neural Network, Random Forest, Regression, Support Vector Machine.

 

ABSTRACT

Machine learning seems to be an artificially intelligent application that demonstrates systems with both the ability to analyze and enhance inherently via experience whilst being specifically programmed. Algorithms rely on software programs that are developed that could also access information and using that to learn for itself. The prediction of rainfall is regarded as very significant in everyday life, from cultivation to event. Previous prediction of rainfall was using the complex combination of mathematical abstractions and it was inadequate to get such a high classification rate Prediction of rainfall is rendered via acquiring quantitative data about the present atmospheric state. Algorithms models could learn complicated mappings, based solely on samples, from inputs to outputs, and require minimal mapping. Due to the dynamic nature of the atmosphere, a precise prediction of weather conditions is a difficult task. To forecast the rainfall state of the future, the variability in situations in earlier years need to be used. The likelihood it will fit throughout the past year's neighboring fortnight is a very high Random forest rainfall prediction algorithm with factors including temperature, humidity, and wind. Therefore this forecast will prove accurate, it will predict rainfall based on previous records.The platform used is anaconda and the language is python which is portable and interactive. The libraries used for implementation are numpy, matlib, seaborn and pandas.

 

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