This project involves the collection and analysis of YouTube song data to help a rapper understand trends and determine the optimal time to release new songs. The analysis includes various visualizations that highlight key metrics such as views, likes, and comments over time.
Web scraping using Selenium and requests to gather song data. Use of YouTube API to fetch additional data. Data cleaning and preprocessing using Pandas. Time series analysis to predict future trends in views and likes. Comprehensive visualizations using Matplotlib and Seaborn.
Web Scraping Fetching Data via YouTube API
Used the YouTube Data API to gather detailed information about each song, including views, likes, and comments.
Cleaning and Preprocessing
Removed duplicates and handled missing values. Extracted relevant information such as the year and month of song release. Trend Analysis
Performed time series analysis to identify trends and patterns in the data. Predicted future views and likes for the next two years using the Facebook Prophet model. Visualizations
Created various plots to illustrate key insights, such as the number of views, likes, and comments over time. Highlighted the top 5 most popular songs and their metrics.
Provided actionable insights on the best times to release new songs based on historical trends. Identified the most popular songs and their characteristics.