Comparison of classification performance of selected algorithms using rural development investments support programme data [Ki{dotless}rsal kalki{dotless}nma yati{dotless}ri{dotless}mlari{dotless}ni{dotless}n desteklenmesi programi{dotless} verileri kullani{dotless}larak seçilen algoritmalari{dotless}ni{dotless}n si{dotless}ni{dotless}flandi{dotless}rma performanslari{dotless}ni{dotless}n karşi{dotless}laşti{dotless}ri{dotless}lmasi{dotless}]

It is not always possible to solve a large size of data via traditional statistical techniques. in order to solve these kinds of data special tactics like data mining are needed. data mining may meet these kinds of needs with both categorizing and piling tactic. in this study,we have used data mining by using rural development investment support program (rdisp) data with various categorizing algorithms. the most prospering categorizing algorithm was tried to determine by using present data. at the end of analysis,it has been understood that mlp (multilayer perceptron),a nerve net model,is the best algorithm that makes the best categorizing.