Comparative Analysis of Weather Prediction Using Classification Algorithm: Random Forest Classifier, Decision Tree Classifier and Extra Tree Classifier.

Publication Date: 17/05/2024

DOI: 10.52589/AJMSS-F6H03BNE


Author(s): Oni Oluwabunmi Ayankemi, Iskilu Zainab Adesola, Lawrence Adeolu.

Volume/Issue: Volume 7 , Issue 2 (2024)



Abstract:

Comparison of machine learning models is carried out in order to determine which models are best to deploy as a system. However, for the purpose of our research we carried out a comparative analysis on Random Forest classifier, Decision Tree classifier and Extra Tree classifier for weather prediction systems as we focused on seeking the classifier with the highest performance metrics. Based on the metrics, accuracy score, the best model for the system was determined. We carried out training, testing and validation of the three different models on the same dataset from the Kaggle dataset. We were able to implement Random Forest Classifier, Decision Tree Classifier and Extra Tree Classifier from Scikit-Learn to make weather prediction and using matplotlib to visualize the accuracy score of the implemented models.  The Random Forest Classifier was chosen as the best able to achieve the highest at 66% accuracy.


Keywords:

Weather, Prediction, Classification, Decision trees, Random forest, Logistic regression, Support vector machine.


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CC BY-NC-ND 4.0