Predicting Life Expectancy of Population Using XGBoost Method
Prediksi Angka Harapan Hidup Penduduk Menggunakan Metode XGBoost
DOI:
https://doi.org/10.21070/ups.3526Keywords:
Asian Countries, Life Expectancy Prediction, XGBoostAbstract
This research aims to predict life expectancy in several Asian countries using the XGBoost Regressor algorithm. The data used is sourced from the UCI Machine Learning Repository. In this study, the researchers construct a predictive model using a machine learning approach and evaluate it based on accuracy and Mean Absolute Error (MAE). The research findings indicate that the XGBoost Regressor model achieves an accuracy rate of 96.8% in predicting life expectancy. The obtained MAE value is 0.97. These results highlight the potential of the XGBoost Regressor algorithm as an effective tool for predicting life expectancy in the Asian region. These findings could have positive implications for data-driven decision-making and welfare policy planning.
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