Case Studies · Open access
Comparative Study On Australia Weather Classification
Abstract
Classification is a key data analysis technique for developing predictive models from labeled data. This study compares the performance of four classification algorithms such as Decision Tree, Neural Network, Naïve Bayes, and Support Vector Machine, applied to the Australian weather dataset. The objective is to predict the next day’s rainfall based on the target variable RainTomorrow. Each model is trained and evaluated using a consistent preprocessing pipeline and evaluated with various performance metrics including accuracy, precision, recall, and F1-score. Our analysis reveals Support Vector Machine (SVM) as the top-performing classifier, offering practical insights for weather-based predictive modeling tasks.
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Misbahou, M. M. (2025). Comparative Study On Australia Weather Classification. International Journal of Advanced Computing and Intelligent System, 3(1), online first. https://doi.org/10.66917/ijacis.a000019
Misbahou, Moubarak Mohamed. “Comparative Study On Australia Weather Classification.” International Journal of Advanced Computing and Intelligent System, vol. 3, no. 1, 2025, pp. online first. doi:10.66917/ijacis.a000019
Misbahou, Moubarak Mohamed. “Comparative Study On Australia Weather Classification.” International Journal of Advanced Computing and Intelligent System 3(1) (2025): online first. https://doi.org/10.66917/ijacis.a000019
Misbahou, M. M. (2025) ‘Comparative Study On Australia Weather Classification’, International Journal of Advanced Computing and Intelligent System, 3(1), pp. online first. doi:10.66917/ijacis.a000019
BibTeX
@article{msisijacis-a000019,
title = {Comparative Study On Australia Weather Classification},
author = {Moubarak Mohamed Misbahou},
journal = {International Journal of Advanced Computing and Intelligent System},
year = {2025},
volume = {3},
number = {1},
doi = {10.66917/ijacis.a000019}
}Related articles
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