Youtube Analysis in Python

Abstract:

Online fraud transaction has been a big concern for e-business platform. As the development of big data technology, e-commerce users always evaluate the sellers according to the reputation scores supplied by the platform. The reason why the sellers prefer chasing high reputation scores is that high reputations always bring high profit to sellers. By collusion, fraudsters can acquire high reputation scores and it will attract more potential buyers. Predicting videos popularity is of great importance for many services and applications ranging from supporting the design and evaluation of a wide range of systems, including the targeted advertising to earn more money, ensure an e ective search and recommendation systems, and design an e ective caching system. The goal of this paper is to use machine learning techniques to predict the popularity of YouTube videos. Here, we present two simple models from machine learning using Logistic Regression Analysis for predicting the popularity of videos based on videos parameters and the proposed popularity function. The experimental results using Logistic Regression Analysis YouTube provide a good accuracy of popularity prediction and show the performance of our approach.