Data mining with Support Vector Machine

Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In this paper introduce SVM. It is techniques and methodologies developed for machine learning tasks Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Support Vector Machine (SVM) is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. Support Vector Machines are state-of-the art data mining techniques which have proven their performance in many applications , such as credit scoring , financial time series prediction , spam categorization and brain tumor classification . The strength of this technique lies with its ability to model nonlinearity, resulting in complex mathematical models. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis.. The classification used in various area one of them is Credit Scoring. The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. Test support vector machines against traditional methods on a large credit card database. From that find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. Keyword: SVM, Credit risk evaluation, feature selection, Data classification, Machine learning