It has been widely argued among researchers that the application of big data analytics promises to reduce human bias and provide a scientific and evidence-based approach to the judicial process. In this dataset, historical data consisting of appeal cases presented at the Supreme Court of Nigeria (SCN) were collected from an online repository (Primsol Law Pavillion). A total of 5585 appeal cases brought before the SCN were collected from the archive. The dataset consisted of both criminal and civil appeal cases brought before the SCN. Variables that are related to court case proceedings were identified from related literature, verified by legal experts and used as a basis for generating an electronic structured version of the dataset stored as a spreadsheet file from the unstructured data. From the collected data, thirteen input variables were identified with one output/decision variable. The distribution of the numerical variables was presented as a descriptive statistical summary in terms of the minimum, maximum, mode, mean and standard deviation. The developed dataset can assist researchers to build predictive systems by training their models. Various feature extraction techniques can also be applied on the dataset to remove irrelevant or redundant features for increased performance of such classifiers that are needed to predict the outcome of legal cases.
Keywords: Classification algorithms; Feature extraction; Justice system; Supervised learning.
© 2023 The Author(s).