Abstract
This paper highlights an attempt for addressing the issue of imbalanced classification resulted due to deployment of machine learning algorithms over an imbalanced dataset. It has used Synthetic Minority Oversampling Technique (SMOTE). This type of augmentation of the dataset is extremely necessary as it leads to poor performance in the minority class. Four machine learning algorithms were deployed on the Twitter dataset using the Python platform. Standard data preprocessing including data cleaning, data integration, data transformations, and data reduction was carried out first as the most necessary arrangement before experimentations.
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Patil, D., Katyare, P., Bhalchandra, P., Muley, A. (2021). Effective Usage of Oversampling with SMOTE for Performance Improvement in Classification Over Twitter Data. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_53
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DOI: https://doi.org/10.1007/978-3-030-69921-5_53
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