Аннотация
In the era of climate changes and ever-changing daily temperatures, we want to know tomorrow's temperature so we may plan our work and dress to prepare ourselves well in advance. In this work, we have used a Random Forest classifier, a Support Vector classifier, a KNN classifier, a Logistic Regression classifier, a Gradient Boosting classifier, a Decision Tree classifier, and an AdaBoost classifier, and the dataset was created from readings of one of the most common IoT devices, i.e., a temperature sensor. When ranking these seven machine learning algorithms based on their accuracy for this specific temperature dataset, the decision tree classifier performs the best in predicting future temperatures. In contrast, the K-nearest neighbour classifier ranks the lowest. The other five machine learning classifiers perform at intermediate levels. When analysing results based on precision, KNN is performing better than the other six algorithms.