000			03963nas|a2200469 a 4500
005			20260817163101.0
008			161121s2012 th uu m rtt 0| a1eng d
035			_a.b12174749
099		9	_aAIT RSPR no.CS-15-02
100	0		_aEi Thaw Win
245	1	0	_aApplying data mining techniques : _bcase studies to predict loan defaults and vehicle quality
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2015
300			_a41 p. : _bill.
490	1		_aResearch studies project report ; _vno. CS-15-02
500			_aA research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science, School of Engineering and Technology
502			_aResearch Studies Project Report (M.Eng.) - Asian Institute of Technology, 2015
520			_aData 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.
650		0	_aData mining
650		0	_aUsed cars _xData processing
700	1		_aGuha, Sumanta, _eExamination Committee
700	0		_aVatcharaporn Esichaikul, _eExamination Committee
700	1		_aDuboz, Raphael, _eExamination Committee
710	2		_aMinistry of Foreign Affair, Norway, _eScholarship donor
810	2		_aAsian Institute of Technology. _tResearch studies project report ; _vno. CS-15-02
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B01315
907			_a.b12174749 _bmnait _cm
902			_a240405
998			_b0 _c161121 _dm _ea _fm _g0
962			_a000:001:PDF:b1217474:002837:0:0:0:0:0:0 _tAbstract _vn
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