Youtube Trending Videos: Boosting Machine Learning Results Using Exploratory Data Analysis

Abstract

With the emergence of social media and the Internet, bulks of data are generated every day in different fields. However, filtering out useful information, which can be turned into knowledge, requires significant effort. This can be achieved using various Big Data Technologies, statistics, data mining and a variety of machine learning algorithms that corporates and businesses are using to improve their performance every day for a long time now. In this paper, we analyze YouTube trending video data that analyses the target that grabs the user’s attention over a relatively short time. We present our analysis by measuring, mining, analyzing and comparing key aspects of time-series YouTube data with respect to its view and audience response statistics from 40 000 trending YouTube videos collected over 205 days. We have performed an exploratory data analysis (EDA) on all of it's aspect to get data insights and used statistics to find similarities between them to understand viewing pattern of different video categories. We also compare and observe the variation of activity over time with the nature of the event that affects the quality of our analysis. We make the viewership pattern clear belonging to each category separately by direct analysis. The results and findings can help us predict the category of the video that could probably be trending next in the upcoming days and help individuals get boosted views and performance when they upload video as per the predictions and analysis.

Practice and Application of Fusion Machine Learning in Data Analysis

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