1. A method for transforming training data to improve data classification, the method comprising: extracting, by a data transforming system, concepts from a training data set, wherein the training data set comprises data records corresponding to one or more categories; computing, by the data transforming system, frequency of occurrence of each concept in each category of the one or more categories; removing, by the data transforming system, one or more concepts from the data records when the frequency of occurrence of a concept in a category is less than a threshold frequency value; computing, by the data transforming system, a percentage contribution of each concept of remaining concepts in each category upon removing the one or more concepts; eliminating, by the data transforming system, concepts, from the remaining concepts, contributing equally to each category based on the percentage contribution of each concept to provide a reformed training data set; and appending, by the data transforming system, a category name to a corresponding data record in the reformed training data set based on a normalized frequency of occurrence of the concept in a category to improve data classification.