Studies on existing methods of analyses are still insufficient due to the lack of investigation of students’ performance pattern. Prior studies found that there is a limit in the existing systems that can be used to make a comparative analysis and monitoring of the student academic performance. To look into this problem, this study explores the possibility of analysing students’ performance pattern in higher institutions. A system based on Benford’s law was designed to analyse students’ performance pattern and the system was tested with students Cumulative Grade Point Average (CGPA). Python programming language, Malplot library, wxpython graphical user interface and Atom text and source code editor were used for developing and testing the application. After a couple of error debugging and fixes, a well-functioning application that met the requirements of the system was achieved. This study also features a spiral model which was adopted as the methodological approach in the development of the Benford’s Analysis system. The interaction between users and the Benford’s Analysis system was described. Furthermore, a brief description of Benford’s law, pattern recognition, students’ performance and stochastic modeling was given.

The understanding of customer incidents and behaviour is crucial to the success of any organization. Evidence from literature shows a prediction pattern of products to customer. These studies predicted product characteristics leaving out the customers characteristics. To address this gap, this study aims to design datamining system and implement it on an electronic commerce organization website. The customer information and history (clickstreams) from the electronic commerce website was used to predict the customers' behaviour. This will give meaningful and usable data patterns to organizations. Python programming language was used to design the datamining system, while PHP, HTML, and JavaScript were used for the e-commerce website. A brief description of the background of e-commerce and data mining, previous work of researchers who have worked on data mining in e-commerce settings, was reviewed and the relationship between their findings and this work was established. The data mining system utilizes consensus clustering technique and the clustering algorithm with a graphical-based approach. Furthermore, the interaction between the data mining system and the customer's dataset on an ecommerce website was defined. Quantitative evidence for determining the number and membership of possible customer behavioural clusters within the dataset was generated.