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Data Normalization and Standardization: Impacting Classification Model Accuracy
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 35 |
| Year of Publication: 2021 |
| Authors: Mani Butwall |
10.5120/ijca2021921669
|
Mani Butwall . Data Normalization and Standardization: Impacting Classification Model Accuracy. International Journal of Computer Applications. 183, 35 ( Nov 2021), 6-9. DOI=10.5120/ijca2021921669
Abstract
In this paper, it was aimed to see the impact of the data normalization on the accuracy of classification model. In first part of this paper, the structure of dataset, features and basic statistical analysis of the data is represented. In this research, the study is done with the medical data set about the patients with the Diabetic disease. In second part of this paper, we present the process of data normalization and the impact of scaling data on the classification model performance. In this research, Deep Learning model is used for classification purpose. The main classification task was to classify whether the patient is diabetic or non-diabetic. Since the data set contains more numerical parameters of different scaling, the main aim of this paper was to investigate the impact of the data normalization (scaling) on the performance of the classification model. The purpose of the study is to show the difference in accuracy achieved by classification model with and without the use of scaling or normalization.
References
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