📊 Trending YouTube Videos Analysis – SQL Project

📌 Overview

This project focuses on analyzing YouTube trending videos using SQL. It demonstrates skills in data cleaning, preprocessing, and insight generation through SQL queries. The dataset includes metadata such as video details, engagement metrics, and category information.

📂 Dataset

• Source: TrendingYTVideos.csv

• Columns:

  • Video ID, Channel ID & Title
  • Publish Date & Time
  • Video Title & Description
  • Category ID & Label
  • Duration (string & seconds)
  • Definition (HD/SD)
  • Engagement Metrics: Views, Likes, Dislikes, Comments

🛠️ Steps Performed

1️⃣ Data Cleaning & Preprocessing

• Converted published_datetime into separate DATE and TIME.
• Replaced NULL in likes, dislikes, and comments with 0.
• Removed rows with missing videoId, viewCount, or durationSec.

2️⃣ Video Engagement & Popularity Analysis

• Top 10 most viewed videos.
• Top 5 most liked videos.
• Engagement Rate: (likes + dislikes + comments) per 1000 views.
• Average views by category.
• Comparison of views between short (<5 min) and long (>15 min) videos.

3️⃣ Content & Category Trends

• Most common video category.
• View distribution between HD & SD videos.
• Top categories by total engagement.
• Daily uploads trend.

4️⃣ Advanced SQL Queries

• Engagement Leaders: Top video per category using RANK().
• Peak Upload Time: Most common upload hour.
• Performance Outliers: Videos with unusually high likes per category.
• High Engagement Flag: Videos with high view-to-like ratios.

📈 Skills Demonstrated

• Data Cleaning & Transformation in SQL
• Aggregations & Grouping
• Window Functions (RANK(), DENSE_RANK())
• Conditional Logic with CASE
• Date & Time Manipulation
• Analytical Query Writing

🚀 How to Run

1. Load the dataset into your SQL database.
2. Execute the provided SQL script (Task 1.sql).
3. Explore the generated insights.