International Postgraduate Research Conference (IPRC)
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/155
Browse
3 results
Search Results
Item A Review of Data Mining Methods for Educational Decision Support(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Kasthuriarachchi, K.T.S.; Bhatt, C.M.; Liyanage, S.R.Data mining is a computer based information system which is devoted to scanning huge data repositories, generate information and discover knowledge. It attempts to uncover data patterns, organize information of hidden relationships, structure association rules and many more operations that cannot be performed using traditional computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries and education is one domain that can benefit from data mining. Application of data mining in education is living in its spring time and preparing for a hot summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. Educational Data Mining develops and adopts statistical methods, machine- learning and data mining methods to study educational data generated basically by students and educational instructors. The main goal of applying data mining in education is largely to improve learning by enabling data driven decision making for improve current educational practices and learning materials. Educational knowledge discovery, in data mining point of view can be seen as a similar process of applying the general knowledge discovery and data mining process and in experimental point of view, it can be seen as an iterative cycle of hypothesis formation, testing and refinement which not just turn data into knowledge but, also to filter the mined knowledge for decision making. There are many applications in education arena that have been resolved using data mining. There are more research studies have also been conducted under various educational problem categories and also there are a number of frequently used data mining methods use in Educational Data Mining. Various open source and commercial tools are available to apply data mining methods on the educational data. This study focuses on the identification of various educational problem domains where data mining methods can be applied and to study the suitability of the available data mining methods and the tools to perform Educational Data Mining in Sri Lankan Educational Institutes. The knowledge discovered by this review is expected to generate meaningful insight and provide guidance for important decisions made by educators.Item Sales Prediction with Data Mining Algorithms(Faculty of Graduate Studies, University of Kelaniya, 2015) Rathnadiwakara, A.S.K.; Liyanage, S.R.Nowadays most business fields using many strategies to improve business profits. Most of them used traditional methods. Therefore, those company‘s efficiency and profit goes to the critical situation. So the improve efficiency of the company is a major requirement for nowadays business platform. Using new technologies companies can improve their profit and efficiency. Also companies can identify their sales life-cycle. This sales prediction was carried for Alfred Edirisinghe (PVT) LTD which is a medium scale tyre dealer in Colombo. Decision Tree, Association Rules and Naïve Bayes data mining models were attempted for the prediction. The best algorithm was selected for each model. Item Code, Item Type, Item Quantity, Item Value, Item Sold Date, etc. variables were used in data mining process. Among those variables five variables were selected for the mining process. A sales data sample with 5000 records were provided by the client for the analysis. Out of the 5000 records 30% was used in the mining process. According to the predicting probabilities, Decision Tree algorithm were performed 98.65%, Association Rules algorithm were performed 100.00% and Naïve Bayes algorithm were performed 99.57%. Decision Tree belongs to the lowest predict probability value. Therefore Decision Tree model was the worst model. Association Rule model contains highest predicted value 100.00%. Therefore it was the best model. Naïve Bayes model was also a good model. The Score results indicate that Decision Trees and Naïve Bayes mining model has the best score 1.00 and followed by Association Rule mining algorithm with score of 0.99. By considering score and target population with predicting probabilities, Association Rule algorithm was the best one for prediction process. Data mining model was implemented using Association Rule algorithm. According to these predicting results, the company can handle their imports optimizing the available resources; storage, time, money. Therefore this research would benefit the Company to improve their incomes.Item A Study on Loan Performance Using Data Mining Techniques(Faculty of Graduate Studies, University of Kelaniya, 2015) Thisara, E.B.; Wijegunasekara, M.C.Most of the modern financial companies offer loans to customers in order to build up their own business. Such companies have a major problem when they recover the loan as the customers do not pay the installments according to the signed contract. It is crucial to determine/create the appropriate strategies and to identify the risk free customers as there is high potential of non-performing loans. In order to predict the risk factors that affect to non-performing loan, Data Mining techniques were considered. This research discovered the factors/reasons for non-performing loan using the data from a reputed Finance Company. This research focused on eighteen attributes which were referred to as factors affecting a nonperforming loan state and the dataset contained with 30% of test data and 70% of training data from 750 records. Among those attributes eleven key attributes namely: Age, Area, Branch Name, Customer Job, Income, Loan State, Mortgage, Number of Terms, Overdue days, Product Type and Interest Rate were selected to create the data mining models. The considered mining models were namely: Neural Networks (NN), Decision Trees (DT) and Clustering (CL). These models were created using the Business Intelligence tool and the database was created in SQL Server Management Studio 2008R2. The predicted probabilities (as a percentage) of Neural Networks, Decision Trees and Clustering models were 1.57%, 0.44% and 10.46% for non-performing loan state respectively. As the Clustering Model had the highest value it was chosen as the best algorithm to evaluate loan state by using Microsoft clustering method. The Clustering model was given ten clusters numbered from 1 to 10 and five clusters namely: 3, 6, 8, 9 and 10 were identified as the most inclined towards the non-performing loan state by comparative analysis. The predicted probabilities of selected clusters were 23%, 41%, 32%, 23% and 35% respectively and cluster number 6 showed a highest value and cluster number 10 showed the next highest value. Based on cluster performance, numbers 1, 2, 4, 5, 7 had a high probability of becoming performing loan and thus were not included in the analysis. According to the states of attributes within each cluster profiles Product Type, Customer Job, Mortgage, Income, Number of Terms and Interest Rate were identified and shortlisted as the factors affecting the nonperforming loan state most. The research identified that if the customer is self-employed or individual, a small property owner, or having a low income and depending on the type of mortgage (building, vehicle or non-mortgage) the loan tend to be non-performing. The longer duration for loan repayment or higher interest rates will also cause a loan to be non-performing. According to the above results it can be concluded that the high interest loans provided for the unemployed customers or customers with low income have a higher potential to be non-performing and hence resulting in a monetary loss for the financial company. Therefore a financial company will be able to improve its profits if they are more concerned about such customers and undertake suitable decisions. The model will support the financial sector in identifying the amount of loans that could be transformed into the non-performing state. Therefore the findings of this research will benefit the financial industry to reduce the risk of granting loans when providing loans in future.