Abstract
The historical geographical data of Kashmir province is spread across two disparate files having attributes of Maximum Temperature, Minimum Temperature, Humidity measured at 12 A.M., Humidity measured at 3 P.M., rainfall besides auxiliary parameters like date, year etc. The parameters Maximum Temperature, Minimum Temperature, Humidity measured at 12 A.M., Humidity measured at 3 P.M. 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
