A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science, School of Engineering and Technology

Research Studies Project Report (M.Eng.) - Asian Institute of Technology, 2015

Data mining is popularly applied to discover ground truths from huge amounts of data.Data mining techniques in this study are applied to two different problems to study the effectiveness of DM tools with different characteristics from the Bank Industry and Used Cars Market, respectively.The first problem to tackle is the recovery of loans in the banking industry that will predict the expected percentage of return on future loans.The second problem considers the automobile industry. It deals with car dealerships purchasing used vehicles at an auction and predicts the likelihood that the vehicle will be a bad buy.The objectives of this research study is applying data mining tools and techniques to solve the above problems and determine the best method suitable for the characteristics of the problem.In this research paper, data mining models which include k-NN, random forest and support vector machine (SVM) are applied on two different datasets.The goal of this study was to compute the performance of data mining techniques on both problems: binary and multiple classification using the evaluation techniques: classification accuracy, precision, recall and root mean square error.According to the experimental results, the SVM model verifies to have the best performance in both problems; loan default prediction and vehicle quality prediction. Random forest model also performs well in both datasets. The performance of K-NN algorithm is better in vehicle quality prediction than loan default prediction.