基于数据分析的诺贝尔奖预测研究
Research on Nobel Prize Prediction Based on Data Analysis

本文旨在通过深入分析1901年至2023年间所有诺贝尔化学奖获得者及其研究成果的数据，应用多种机器学习算法，构建预测模型，以优化未来诺贝尔化学奖得主的预测准确性。研究过程中，我们不仅收集了正面样本(诺贝尔化学奖得主)的数据，还构建了负样本数据集(沃尔夫化学奖获得者但未获诺贝尔化学奖者)。通过跨学科奖项的比较分析、机器学习模型的开发与应用，以及可视化展示，预测了未来可能的诺贝尔化学奖得主：Shankar Balasubramanian、Roberto Car、Vladimir P. Torchilin。本研究为理解诺贝尔化学奖的评选机制、获奖者的学术背景提供了新的视角。
This paper aims to build a prediction model to optimize the prediction accuracy of future Nobel Prize winners in chemistry by deeply analyzing the data of all Nobel Prize winners in chemistry and their research achievements between 1901 and 2023, applying a variety of machine learning algorithms. During the study, we not only collected data on positive samples (Nobel Prize winners in chemistry), but also constructed a negative sample dataset (Wolf Prize winners in Chemistry but no Nobel Prize winners in chemistry). Through the comparative analysis of interdisciplinary awards, the development and application of machine learning models, and the visual presentation, the possible future Nobel Prize winners in chemistry were predicted: Shankar Balasubramanian, Roberto Car, Vladimir P. Torchilin. This study provides a new perspective for understanding the selection mechanism of the Nobel Prize in Chemistry and the academic background of the winners.