Performance Analysis of Predictive Models using Generic Datasets
Artika Singh1, Manisha Jailia2, Shubhangi Jain3
1Artika Singh, Perusing, Ph.D, Department, computer science and Engineering, Banasthali Vidhyapith
2Dr. Manisha Jailia, Associate Professor, Department, computer science Banasthali Vidhyapith.
3Shubhangi Jain, Pursuing, B.Tech, Computer Engineering from MPSTME, NMIMS.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 28, 2019. | Manuscript published on January 10, 2020. | PP: 3612-3617 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8358019320/2020©BEIESP | DOI: 10.35940/ijitee.C8358.019320
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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: Today over 2.5 quintillion bytes of data is being created every single day where 753 crore people on this planet are creating 1.7mb of data each second. Most often than not, Researchers only scratch the surface when it comes to analyzing which algorithm will be best suited with their dataset and which one will give the highest efficiency. Sometimes, this analysis takes more computational time than the actual execution itself. Aim of this paper is to understand and solve this dilemma by applying different predictions models like Neural Networks, Regression and Decision Tree algorithms to different datasets where their performance was measured using ROC Index, Average Square Error and Misclassification Rate. A comparative analysis is done to show their best performance in different scopes and conditions. All data sets and results were compared and analyzed using SAS tool.
Keywords: Prediction Model, Decision Tree, Regression, Neural Network, ROC Index, Average Square Error, Mis Classification Rate
Scope of the Article: Classification