  Rainfall prediction is one of the challenging and uncertain tasks which has a
significant impact on human society. Timely and accurate predictions can help
to proactively reduce human and financial loss. This study presents a set of
experiments which involve the use of prevalent machine learning techniques to
build models to predict whether it is going to rain tomorrow or not based on
weather data for that particular day in major cities of Australia. This
comparative study is conducted concentrating on three aspects: modeling inputs,
modeling methods, and pre-processing techniques. The results provide a
comparison of various evaluation metrics of these machine learning techniques
and their reliability to predict the rainfall by analyzing the weather data.
