Dataset of Appeal Cases heard at the Supreme Court of Nigeria

The dataset contains information about appeal cases heard at the Supreme Court of Nigeria (SCN) between the years 1962 to 2022. The dataset was extracted from case files that were provided by The Prison Law Pavillion; a data archiving firm in Nigeria. The dataset originally consisted of documentation of the various appeal cases alongside the outcome of the judgment of the SCN. Feature extraction techniques were used to generate a structured dataset containing information about a number of annotated features. Some of the features were stored as string values while some of the features were stored as numeric values. The dataset consists of information about 14 features including the outcome of the judgment. 13 features are the input variables among which 4 are stored as strings while the remaining 9 were stored as numeric values. Missing values among the numeric values were represented using the value -1. Unsupervised and Supervised machine learning algorithms can be applied to the dataset for the purpose of extracting important information required for gaining a better understanding about the relationship that exists among the features and with respect to predictng the target class which is the outcome of the SCN judgment.

本数据集收录了1962年至2022年间尼日利亚最高法院（Supreme Court of Nigeria, SCN）审理的上诉案件相关信息。该数据集提取自尼日利亚数据存档机构监狱法律馆（The Prison Law Pavillion）提供的案件卷宗。数据集最初包含各类上诉案件的文档记录及尼日利亚最高法院的判决结果。研究人员通过特征提取技术构建了结构化数据集，其中包含多项经标注的特征信息。部分特征采用字符串类型存储，其余特征采用数值类型存储。本数据集共涵盖14项特征信息，其中包含判决结果这一目标变量；13项为输入特征，其中4项为字符串类型，剩余9项为数值类型。数值型特征中的缺失值以-1作为占位标记。可针对该数据集应用监督学习与无监督学习机器学习算法，以提取关键信息，从而更深入地理解特征间的内在关联，并预测作为目标类别的尼日利亚最高法院判决结果。