Rainfall Prediction using Machine Learning Algorithms

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. This paper investigates the effectiveness of two machine learning algorithms - Logistic Regression (LR) and Random Forest (RF) - for rainfall prediction using historical datasets. This study would assist researchers in analysing 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. The main objective of this paper is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. This project focuses on harnessing the power of basic machine learning algorithms to unravel insights from a historical rainfall dataset. Insights are interpreted through feature importance analysis and visualization of decision tree structures. In essence, this project showcases the efficiency of basic machine learning algorithms in uncovering hidden information within a rainfall dataset.