Appeal Cases heard at the Supreme Court of Nigeria Dataset
Description
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 of the relationship that exists among the features and with respect to predicting the target class which is the outcome of the SCN judgment.
Files
Steps to reproduce
Information about the features that are associated with the determination of the outcome of the judgment of appeal cases made by the SCN was determined from related research studies. Historical files consisting of information about appeal cases were collected following which information about the identified features was extracted from the dataset. By doing this, the unstructured nature of the information stored in the case files which were stored as documents (.docx) was converted into a structured form which was stored in a spreadsheet file format. The structured dataset consisted of the extracted features as columns while the information for a set of features that was extracted from each case was presented as a row (or instances). Relevant features among the initially identified features which may provide more insight into understanding the outcome of judgments can be identified using feature selection techniques. Supervised machine learning algorithms can be applied to the dataset for the development of predictive models which can be used to determine the outcome of the judgment based on information collected about the identified features.