A Comparative Analysis for the Detection of Hit Rate of Popular Music Videos in Social Network using Logistic Regression over Support Vector Machine Algorithm

The objective of the work is to improve the innovative music videos popularity prediction in social networks based on hit rate using machine learning algorithms like Logistic Regression algorithm and Support Vector Machine (SVM) algorithm. Materials and Methods: SVM algorithm and Logistic Regression algorithm with sample size N = 10 was iterated 20 times for predicting innovative music videos popularity on social media. Result: The support vector machine algorithm produces a better accuracy of (87.6%) than logistic regression classifier (81.6%). The significance (p<0.03) of the support vector machine algorithm two samples T test is high. Conclusion: Within the limits of the study the results proved that the SVM is significantly better for improving music videos popularity in social networks compared to the Logistic Regression algorithm.