Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/113
Title: An Improved Prediction System for Football a Match Result
Authors: Igiri, Chinwe Peace
Nwachukwu, Enoch Okechukwu
Keywords: ANN, data mining, KDD, models, prediction
Issue Date: Dec-2014
Publisher: Journal of Engineering
Citation: Igiri, C.P. & Nwachukwu, E. O.(2014). An Improved Prediction System for Football a Match Result. IOSR Journal of Engineering (IOSRJEN), ISSN (p): 2278-8719 Vol. 04, Issue 12 PP 12-20
Series/Report no.: 4;12
Abstract: Predictive systems have been employed to predict events and results in virtually all walks of life. Football results prediction in particular has gained popularity in recent years. Statistical approaches have shown complex and low prediction results. Data mining tools with insufficient features, however, have also yielded low predictions. In our research, knowledge discovery in databases (KDD) has been used to develop a football match result predictive model by gathering 9 features that affect the outcome of football matches. We constructed a more comprehensive system with an improved prediction accuracy by using the features that directly affect the result of a football match. Our prediction system for football match results was implemented using both artificial neural network (ANN) and logistic regression (LR) techniques with Rapid Miner as a data mining tool. The technique yielded 85% and 93% prediction accuracy for ANN and LR techniques respectively. With this output, it is observed that the prediction accuracy is higher than those of existing systems.
URI: http://localhost:8080/xmlui/handle/123456789/113
ISSN: 2278-8719
Appears in Collections:Computer Science

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