Efficient Rainfall Prediction and Analysis using Machine Learning Techniques

Abstract

Rainfall prediction is a beneficiary one, but it is a challenging task. Machine learning techniques can use computational methods and predict rainfall by retrieving and integrating the hidden knowledge from the linear and non-linear patterns of past weather data. Various tools and methods for predicting rain are currently available, but there is still a shortage of accurate results. Existing methods are failing whenever massive datasets are used for rainfall prediction. This study provides efficient rainfall prediction methods of machine learning techniques: random forest and logistic regression methods that provide an easy and accurate prediction and determine which one is more effective in comparison. This study would assist researchers in analyzing the most recent work on rainfall prediction with an emphasis on machine learning techniques and providing a reference for possible guidance and comparisons. Anaconda framework is used, and the coding language used is Python, which is portable and dynamic. Numpy, matplotlib, seaborn, and pandas are the libraries used for the implementation.