Predicting the severity of future earthquakes by employing the random forest algorithm

Publication Name

Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science


Random forest regression is an ensemble, supervised learning algorithm capable of executing both classification and regression. Within this report, the use of the following algorithm will be implemented on an earthquake dataset which consists of all recorded occurrences of earthquakes from 1930 to 2018. Certain columns from the database will be used as target variables such as magnitude and depth to predict the following outcome based on trained data. Hyper parameter tuning will be performed to maximize the model's performance by increasing its accuracy, decreasing errors, and ensuring efficiency. The parameter in this model that contributed to the efficiency while performing hyper parameter tuning was number of estimators. Findings from the research report concluded that the model's accuracy levels were approximately 75%. Despite increasing the number of trees used, the model's accuracy did not significantly change and improve but rather significantly slowed down the run-time.

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