Fashion design is the applied art devoted to the designing of clothing and lifestyle accessories. This art is influenced by cultural and social attitudes and has evolved over time and place. Fashion designing has come a long way from the designer clothes worn by the royalty of the ancient world to the haute couture products of the present.

Fashion designing is one of the most lucrative, appealing, glamorous and exciting career options in today's world. If you have a penchant for creativity, style and originality, a career in fashion designing is the one tailor made for you. On one hand, the fashion industry satisfies both the creative fancies and the materialistic needs of the people. On the other hand, it promises glamour, fame, success and a high pay package to the talented people.

However, it is also a demanding career, as fashion designers need to combine their creativity with managerial skills to sustain in this industry. Thus, if you can create magic with colours, shapes and designs, then just obtain a professional course to begin a successful career in the alluring world of fashion designing.

The objective of doing this research is to find the patterns and characteristics of students admitted and to predict the future choice of the course. So huge data needs to be processed and patterns need to be compared manually, which is tedious and cumbersome. Hence data mining models are used to mine the large data using algorithms like Nave Bayes, K-means, and Decision tree to interpret potential and beneficial data. Data was obtained from NIFTcollege in excel format from 2010 to 2013.Data in the excel format were fed to MYSQL in the form of queries and two databases were constructed, One containing historic data from 2010 to 2013 and another test data i.e., 2014.

Decision Trees are considered to be one of the most popular approaches for rep-resenting classifiers. Researchers from various disciplines such as statistics, ma-chine learning, pattern recognition, and Data Mining have dealt with the issue ofgrowing a decision tree from available data. This paper presents an updated sur-vey of current methods for constructing decision tree classifiers in a top-downmanner. The chapter suggests a unified algorithmic framework for presentingthese algorithms and describes various splitting criteria and pruning methodolo-gies