Machine Learning: Australian Rainfall Prediction

Abstract: Rainfall prediction plays a critical role in agriculture, water resource management, and disaster preparedness. This project focuses on predicting rainfall in Australia using a comprehensive weather dataset containing meteorological variables such as temperature, humidity, wind speed, pressure, and historical rainfall records. The research problem addressed in this study is the uncertainty and inaccuracy of traditional forecasting methods, which often fail to capture complex, non-linear weather patterns. The main objectives of this study were to preprocess the dataset, handle missing values, apply data balancing techniques using SMOTE, and implement machine learning models for accurate rainfall forecasting. Various classification algorithms were applied, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. The models were evaluated using accuracy, precision, recall, and F1-score. The findings indicate that ensemble methods performed significantly better compared to simple classifiers. Among all models, Random Forest achieved the highest accuracy of approximately 85%, with humidity, temperature, and wind-related features emerging as the most influential predictors