Rainfall Prediction using XGB Model with the Australian Dataset

Surendra Reddy Vinta; Rashika Peeriga

doi:10.4108/ew.5386

Authors

Surendra Reddy Vinta Vellore Institute of Technology University
Rashika Peeriga Marri Laxman Reddy Institute of Technology and Management

DOI:

https://doi.org/10.4108/ew.5386

Keywords:

Rainfall prediction, Machine Learning, Randomforest, Logistic regression, Gaussian Naive Bayes, K-nearest neighbors, Support Vector classifier, XGBoost, Accuracy, Cross-validation, Performance metrics, Australian rainfall dataset, Data preprocessing, Feature selection, Missing value imputation, Practical applications

Abstract

Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively.

With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.

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