GitHub repository: Greeshma-Venkatesh/Data-245-ML-Project_-YouTube-Trend-Analysis
Primary language: Jupyter Notebook
Created: 2021-11-28T07:39:02Z
Description: YouTube is one of the most popular video-sharing websites on the internet which is open source. It is the biggest platform with ample video categories in the world. The content includes a variety of music, product promotion, blogs, reviews, educational and entertainment videos, etc. It also includes various features that will help the users to explore and look into an enormous number of videos based on their choices and likings. There are several features that make the video trending and gain the most popularity among the audience. Some of them are the view count and how quickly the videos are getting views. We also consider a fact to analyze various other sites through which the same video is being viewed repeatedly. The understanding of features and deriving insights to predict the popularity of a video is very essential in the current times. Although YouTube is very famous and important, there is still an opportunity to identify and analyze significant insights. The aim of this project is to include interactive features to compare the correlation for the trend of the videos. We have called the live streaming data through API directly from the YouTube storage portal. Our objective is to work on the US region of YouTube data. The objective of the project is to perform machine learning models for three different target variables to fully grasp the features that affect video popularity. For this purpose, we are going to analyze the trending data and compare them through machine learning algorithms like Linear Regression for the Views analysis. Logistic Regression, Naive Bayes, Random Forest, Decision Tree, KNN for the Category prediction and number of days required for a video to be trending list prediction. The implemented models are compared and evaluated to see which model gives the best accuracy on given data.