R.Ranjani Priyaa

YOUTUBE TRENDING VIDEO ANALYTICS 1. Introduction This project focuses on analyzing trending YouTube videos to uncover key insights such as engagement rate, regional performance, and content popularity patterns. The dataset was collected directly using the YouTube Data API v3, which provided real-time data for trending videos across different regions. This approach ensured accuracy and freshness of information for deeper insights. 2. Abstract The project explores YouTube’s trending video data to analyze performance metrics including views, likes, comments, and engagement ratios. Using tools like Python, PostgreSQL, and Tableau, data was cleaned, stored, and visualized to reveal patterns in audience engagement and regional trends. The analysis supports creators and marketers in optimizing content strategy based on data-driven insights. 3. Tools Used • YouTube Data API v3 – for data collection • Python (Pandas, Requests) – for API calls, cleaning, and preprocessing • PostgreSQL – for structured data storage and querying • Tableau – for visualization and dashboard creation • Word – for report generation 4. Steps Involved 1. Data Collection o Used YouTube Data API v3 to extract trending video details like title, channel, view count, likes, and comments across multiple regions. o Saved API responses as JSON and converted them into CSV format. 2. Data Cleaning and Transformation o Used Python (Pandas) to clean missing and inconsistent data. o Ensured correct numeric types (views, likes, comments) and standardized date/time fields. 3. Database Integration o Created a table in PostgreSQL and imported the cleaned dataset using SQL COPY command. 4. Exploratory Data Analysis o Analyzed engagement trends using Python (matplotlib, seaborn) and SQL queries. o Derived new metrics like Like-to-View Ratio and Comment Rate. 5. Dashboard Building in Tableau o Upload the .csv file saved from python and Postgre SQL. o Created visuals: Publishing Trends Over Time, Top Channels, Sentiment Impact on Views, Engagement Rate by Channel, and Regional Metrics. o Added filters and color encodings for better interactivity. 5. Conclusion The YouTube Trending Analysis successfully demonstrated how real-time data from the YouTube API can be transformed into actionable insights. The findings highlight that engagement rates vary significantly by region and channel type, offering valuable guidance for creators and marketers aiming to optimize their video strategies.