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The historical geographical data of Kashmir province is spread across two disparate files having attributes of Maximum Temperature, Minimum Temperature, Humidity measured at 12 AM, Humidity measured at 3 PM, rainfall besides auxiliary parameters like date, year etc. The parameters Maximum Temperature, Minimum Temperature, Humidity measured at 12 AM, Humidity measured at 3 PM are continuous in nature and here, in this study, we applied Information Gain and Gini Index on these attributes to convert continuous data into discrete values, their after we compare and evaluate the generated results. Of the four attributes, two have same results for Information Gain and Gini Index; one attribute has overlapping results while as only one attribute has conflicting results for Information Gain and Gini Index. Subsequently, continuous valued attributes are converted into discrete values using Gini index. Irrelevant attributes are not considered and auxiliary attributes are labeled accordingly. Consequently, the data set is ready for the application of machine learning (decision tree) algorithms.