标题： 危险小行星分类 显示英文标题

标题： Hazardous Asteroids Classification

摘要： 近地小行星一直是人类关注的问题之一，因为小行星坠落到地球可能会对社会造成巨大影响。监测这些天体有助于预测未来的撞击事件，但此类努力受到地球附近大量物体的阻碍。 本项目的目标是利用机器学习和深度学习技术，准确分类近地危险小行星。 我们训练并评估了总共十种方法，其中包括五种机器学习算法和五种深度学习模型，以寻找能够解决该问题的合适模型。 我们使用了两个数据集进行实验，一个来自Kaggle，另一个是从NASA提供的RESTful网络服务NeoWS（一个提供近地小行星信息的网络服务）提取的，该服务每天更新。 总体而言，模型在具有不同特征的两个数据集上进行了测试，以找到最准确的分类模型。 显示英文摘要

摘要： Hazardous asteroid has been one of the concerns for humankind as fallen asteroid on earth could cost a huge impact on the society.Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth's vicinity. The aim of this project is to use machine learning and deep learning to accurately classify hazardous asteroids. A total of ten methods which consist of five machine learning algorithms and five deep learning models are trained and evaluated to find the suitable model that solves the issue. We experiment on two datasets, one from Kaggle and one we extracted from a web service called NeoWS which is a RESTful web service from NASA that provides information about near earth asteroids, it updates every day. In overall, the model is tested on two datasets with different features to find the most accurate model to perform the classification.