Machine Learning-Based Next-Day Rainfall Prediction System for Australia: A Complete Practice from Data Cleaning to Model Optimization

This article introduces a complete project for building a next-day rainfall prediction system using historical meteorological data from Australia, covering the full workflow including data cleaning, feature engineering, exploratory data analysis (EDA), comparison of multiple classification models, and hyperparameter optimization, providing practical references for meteorological prediction-related machine learning projects.

Australia has a vast territory and diverse climate; accurate next-day rainfall prediction has practical value for agriculture, tourism, and daily life. The core objectives of the project include: predicting next-day rainfall, analyzing meteorological patterns, identifying key predictive variables, comparing the performance of different machine learning algorithms, and evaluating model effectiveness from multiple dimensions.

The project uses historical observation data from multiple meteorological stations in Australia. Core features include temperature, rainfall, humidity, wind force, air pressure, cloud cover, geographical location, and season. The target variable is the binary RainTomorrow (whether it will rain the next day). The preprocessing workflow includes missing value handling, outlier cleaning, feature selection, categorical variable encoding, numerical standardization, and dataset splitting.

EDA findings: Indicators such as rainfall and humidity show skewed distributions; humidity, air pressure, and same-day rainfall are highly correlated with next-day rainfall; features with excessively high missing rates are removed, and those with moderate missing rates are filled via interpolation; the frequency of rainfall in coastal areas is higher than in inland areas, and seasonal changes have a significant impact.

Algorithms such as Random Forest (with GridSearchCV hyperparameter tuning) and Logistic Regression (as the baseline model) are implemented. Evaluation metrics include accuracy, precision, recall, F1 score, confusion matrix, and cross-validation scores. Random Forest performed the best, while Logistic Regression has strong interpretability.

Key findings: Random Forest has strong adaptability to high-dimensional meteorological data; humidity, same-day rainfall, and air pressure changes are strong predictive factors; feature engineering significantly improves model quality; the model has stable generalization. Technology stack: Python, Pandas, NumPy, Matplotlib, Scikit-Learn, Jupyter Notebook. The project structure includes the main Notebook, README, and dependency list.

Current limitations: Only uses historical data, relies on data quality, and has limited feature dimensions. Future improvements: Introduce models such as XGBoost, integrate real-time meteorological APIs, deploy web applications, automate retraining, and add interpretable AI technologies like SHAP/LIME.

The project fully demonstrates the complete workflow from raw data to a deployable model, with a clear structure and comprehensive documentation, providing references for similar projects. Usage guide: Clone the repository → Install dependencies → Launch the Notebook → Run the analysis.