Performance Analysis of Predictive Models using Generic Datasets

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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.

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Singh*, A., Jailia, Dr. M., & Jain, S. (2020). Performance Analysis of Predictive Models using Generic Datasets. International Journal of Innovative Technology and Exploring Engineering, 9(3), 3612–3617. https://doi.org/10.35940/ijitee.c8358.019320

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