7. A data transforming system for transforming training data to improve data classification, the data transforming system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to: extract concepts from a training data set, wherein the training data set comprises data records corresponding to one or more categories; compute frequency of occurrence of each concept in each category of the one or more categories; remove 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; compute a percentage contribution of each concept of remaining concepts in each category upon removing the one or more concepts; eliminate 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, wherein eliminating concepts contributing equally to each category comprises eliminating concepts corresponding to a row in an asymmetry matrix from the training data set when a maximum distance of distances in the row of the asymmetry matrix is less than a pre-defined contribution value; and append 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.
7. A data transforming system for transforming training data to improve data classification, the data transforming system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to: extract concepts from a training data set, wherein the training data set comprises data records corresponding to one or more categories; compute frequency of occurrence of each concept in each category of the one or more categories; remove 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; compute a percentage contribution of each concept of remaining concepts in each category upon removing the one or more concepts; eliminate 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, wherein eliminating concepts contributing equally to each category comprises eliminating concepts corresponding to a row in an asymmetry matrix from the training data set when a maximum distance of distances in the row of the asymmetry matrix is less than a pre-defined contribution value; and append 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. 12. The data transforming system of claim 7 , wherein appending the category name to the corresponding data record biases a classifier to classify a data record in the category corresponding to the category name.