Performance Evaluation of Machine Learning Models for Weather Forecasting

Abstract

Temperature is used to indicate variability and climate changes that indicate the process which is been carried out within the ecosystem and its services. The lack of knowledge about temperature affects human lives in terms of agriculture, transportation, mining, etc. temperature forecasting is used to predict atmospheric conditions based on parameters that caused the temperature to change. This study aims to explore the use of machine learning models for the prediction of temperature, evaluate the performance of these models, and use the model to predict temperature. In this study we explore the use of four different machine learning algorithms for forecasting weather temperature, the algorithms are: Ridge, Random Forest, Linear Regression, and Decision tree. We divided the dataset into training and testing sets, The models were tested on 1000 testing sets based on RMSE score with Decision Tree having the best score of 0.036, Random Forest: 0.208 while Logistic Regression and Ridge had the lowest score of 0.759 respectively.