Application For Comparison and Analysis of Rural to Urban IQ-Regions Based Study using the Data
Sudharshan Duth P1, Sudarshan Ganapati Bhat2, Prajwal M L3, Prasanna Hegde4, John Joseph Jacgnet5
1Sudharshan Duth P, BCA Student, Amrita Vishwa, Vidyapeetham, Mysuru (Karnataka), India.
2Sudarshan Ganapati Bhat, studied BCA at Amrita Vishwa, Vidyapeetham, Mysuru (Karnataka), India.
3Prajwal M L, Assistance Professor in the Department of Computer Science at Amrita Vishwa, Vidyapeetham, Mysuru (Karnataka), India.
4Prasanna Hegde, studied BCA at Amrita Vishwa, Vidyapeetham, Mysuru (Karnataka), India.
5John Joseph Jacgnet, studied BCA at Amrita Vishwa, Vidyapeetham, Mysuru (Karnataka), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1828-1832 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7616068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting IQ in different Rural and Urban areas and comparing their respective education systems is very much challenging due to a set of factors affecting the results. In a country like India, there is a lack of existing system that analyzes and monitors the student profile. Here the IQ is generally suggested on the basis of one’s own performance and social influence; to overcome this drawback of IQ prediction from student’s profile we have come up with the solution which uses Data Mining Methodologies like Chi-square, Naïve Bayesin predicting and classifying IQ. Factors like answerability and the thinking capacity of the targeted student set is taken into consideration in the proposed model.In this paper we also suggest parameters and survey procedures by existing schools; the data collected will be used for comparing and identifying the regions with different IQ levels and in need of attention based on the results acquired
Keywords: Data Mining, Information, IQ Test, Prediction Algorithm, Mental age, Chi-square, Naïve Bayes.
Scope of the Article: Data Mining