Comparative Analyses of Machine Learning Paradigms for Operators’ Voice Call Quality of Service

Mebawondu, Jacob O.

doi:10.1007/978-3-030-69143-1_6

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In another field setting, SVM based on Radial Basic Function attained an accuracy of 0.93 on credit risk prediction (Alabi et al, 2020). Additionally, previous research in various disciplines in the study of Maskeliunas et al 2020, Awotunde et al 2020, and Mebawondu 2020 have proven the overall effectiveness of SVM.…”

Section: Supervised Machine Learningmentioning

confidence: 87%

Enhancing Conversions and Lead Scoring in Online Professional Education

Yim,

Khaw,

Lim

et al. 2024

IJOMFA

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This study seeks to enhance lead conversion for online professional education providers by using supervised machine learning algorithms for lead conversion targeting and lead scoring, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forst, Bagging, Boosting, and Stacking. A lead dataset was used to train and test the machine-learning models. The Recursive Feature Elimination (RFE) is used to establish a precise lead profile. The performance of the trained lead conversion models was evaluated and compared using the 10-Folds cross-validation method based on accuracy, precision, recall, and F1-score. The results show that Stacking is the best model with an accuracy of 0.9233, precision of 0.9391, and F1-score of 0.8939. Meanwhile, the Logistic Regression-based lead scoring model demonstrated promising potential for automating lead scoring. The results of the Logistic Regression-based lead scoring model achieved an accuracy of 0.9019, recall of 0.9019, precision of 0.9015, and F1-score of 0.9014. The optimal lead scoring threshold is 0.20, which stroked the optimal trade-off balance between accuracy, sensitivity, and specificity.

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