porto-seguro

Training dataset of the 'Porto Seguros Safe Driver Prediction' Kaggle challenge [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction]. The goal was to predict whether a driver will file an insurance claim next year. The official rules of the challenge explicitely state that the data may be used for 'academic research and education, and other non-commercial purposes' [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/rules]. For a description of all variables checkout the Kaggle dataset repository [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data]. It states that numeric features with integer values that do not contain 'bin' or 'cat' in their variable names are in fact ordinal features which could be treated as ordinal factors in R. For further information on effective preprocessing and feature engineering checkout the 'Kernels' section of the Kaggle challenge website [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/kernels]. Note that many Kagglers removed all 'calc' variables as they do not seem to carry much information.

本数据集为Kaggle竞赛「Porto Seguro安全驾驶员预测」（Porto Seguro Safe Driver Prediction）的训练数据集，竞赛链接为[https://www.kaggle.com/c/porto-seguro-safe-driver-prediction]。本次竞赛的目标为预测驾驶员次年是否会提交保险索赔申请。竞赛官方规则明确规定，该数据集可用于学术研究、教育及其他非商业用途，规则链接为[https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/rules]。如需了解所有变量的详细说明，请参阅Kaggle数据集仓库：[https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data]。该仓库指出，变量名称中未包含「bin」或「cat」的整数值型特征实则为序数特征，可在R语言中将其作为有序因子处理。如需了解有效的预处理与特征工程方法，请参阅Kaggle竞赛页面的「Kernels」板块，链接为[https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/kernels]。请注意，众多Kaggle竞赛参与者均移除了所有以「calc」命名的变量，因这类变量似乎未携带有效信息。