Road Accident Data Analysis: Data Preprocessing for Better Model Building
In this study we focused on the relationship between preprocessing and model accuracy. The performance of the Machine learning techniques depends on the quality of the data set. Preprocessing is not only advantageous but it is very necessary and a preliminary work in predicting model.
As a result, experiments discovered that preprocessed techniques increased performance for model building. To see the performance preprocessing support vector machine is applied before preprocessing and after preprocessing. Its model accuracy increased from 68.7% to 88.5%.
Keywords: Accuracy; Encoder; Machine Learning; Missing Values; Outliers; Precision; Preprocessing; Road Accidents Data
Document Type: Research Article
Affiliations: Department of Computer Science, Sharda University, Greater Noida 201310, UP, India
Publication date: 01 September 2019
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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