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Comparative Analysis of K-Nearest Neighbours Algorithm and Naive Bayes Algorithm for Prediction of Storm Warning

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  • Challa Rohini
  • S. Magesh Kumar

Abstract

The primary aim of this research was to enhance the accuracy of storm warnings by employing the novel KNearest Neighbours algorithm and comparing it to the Naive Bayes method. This investigation dividedparticipants into two groups: the Novel K-Nearest Neighbours and the Naive Bayes Algorithm, eachcomprising ten representatives. The mean accuracy was determined using the ClinCalc software tool in asupervised learning setting, considering an alpha value of 0.05, a G-Power of 0.8, and a 95% confidenceinterval. The K-Nearest Neighbours algorithm showcased a notable accuracy rate of 68.20%, outstripping the57.31% accuracy of the Naive Bayes. The difference between the two was statistically significant (p=0.000).In conclusion, the K-Nearest Neighbours approach substantially surpassed the Naive Bayes.

Suggested Citation

  • Challa Rohini & S. Magesh Kumar, 2024. "Comparative Analysis of K-Nearest Neighbours Algorithm and Naive Bayes Algorithm for Prediction of Storm Warning," SPAST Reports, SPAST Foundation, vol. 1(3).
    • Handle: RePEc:bps:jspath:v:1:y:2024:i:3:id:4917
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    File URL: https://spast.org/article/view/4917/304
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