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The process of predicting student performance has become a crucial factor in academic environment and plays significant role in producing quality graduates. Several statistical and machine learning algorithms have been proposed for analyzing, predicting and classifying student performance. However, these classification algorithms still posed issue in terms of the performance classification. This paper presents a method to predict student performance using Iterative dichotomiser 3 (ID3), C4.5 and Classification and Regression tree (CART). The experiment was performed on Waikato Environment for Knowledge Analysis (Weka). The experimental results showed that an ID3 accuracy of 95.9% , specificity of 95.9%, precision of 95.9%, recall of 95.9%, f-measure of 95.9% and incorrectly classified instance of 3.83. The C4.5 gave an accuracy of 98.3%, specificity of 98.3%, precision of 98.4%, recall of 98.3%, f-measure of 98.3% and incorrectly classified instance of 1.70. The CART results showed an accuracy of 98.3%, specificity of 98.3%, precision of 98.4%, recall of 98.3%, f-measure of 98.3% and incorrectly classified instance of 1.70. The time taken to build the model of ID3 is 0.05 seconds, C4.5 is 0.03 seconds and CART of 0.58 seconds. Experimental results revealed that C4.5 outperforms other classifiers and requires reasonable amount of time to build the model.

Diabetes Retinopathy is a disease which results from a prolonged case of diabetes mellitus and it is the most common cause of loss of vision in man. Data mining algorithms are used in medical and computer fields to find effective ways of forecasting a particular disease. This research was aimed at determining the effect of using feature selection in predicting Diabetes Retinopathy. The dataset used for this study was gotten from diabetes retinopathy Debrecen dataset from the University of California in a form suitable for mining. Feature selection was executed on diabetes retinopathy data then the Implementation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron (MLP) and Support Vector Machines was conducted on diabetes retinopathy data with and without feature selection. There was access to the algorithms in terms of accuracy and sensitivity. It is observed from the results that, making use of feature selection on algorithms increases the accuracy as well as the sensitivity of the algorithms considered and it is mostly reflected in the support vector machine algorithm. Making use of feature selection for classification also increases the time taken for the prediction of diabetes retinopathy.

Cumulative grade point average (CGPA) is a system for calculation of GPA scores and is one way to determine a student's academic performance in a university setting. In Nigeria, an employer evaluates a student's academic performance using their CGPA score. For this study, data were collected from a student database of a private school in the south-west geopolitical zone in Nigeria. Regression analysis, correlation analysis, and analysis of variance (F-test) were employed to determine the study year that students perform better based on CGPA. According to the results, it was observed that students perform much better in year three (300 Level) and year four (400 Level) compared to other levels. In conclusion, we strongly recommend the private university to introduce program that will improve the academic performance of students from year one (100 level).

<span>Stores today still make use of manual approaches to keeping inventory which could be cumbersome. Having a computerized inventory system would make inventory management more efficient and effective. In this chapter, an Inventory Management System using Association Rule was developed which will ensure proper record keeping and keep items in stocks updated. ANGULARJS, a JavaScript framework, was used for the implementation of the system, PHP (hypertext pre-processor) was used for the backend of the system development as well as the database management, HTML was used alongside CSS for the system interface design and NoSQL database was the database used for this research. In conclusion, a computerized inventory system that had been improved using the Association Rule method was the resulting product useful for creating transactions, updating items in stock, record keeping, generating reports for decision making, and lastly, the system will make the stores more effective.</span>