🚀 YouTube Trending Video Analytics and Virality Estimation

🎯 YouTube Trending Video Analytics

Project Purpose

The Tube Virality project aims to collect, analyze, and model YouTube trending video data across multiple countries using the YouTube API. It aims to gather metrics like view counts, likes, and descriptions, then apply data science techniques to predict a video's likelihood of going viral.

Key Objectives

• ✅ Collect Data via YouTube API: Fetch trending videos and their statistics
• ✅ Build Historical Database: Track daily metrics for trending videos across countries
• ✅ Identify Virality Patterns: Analyze what characteristics correlate with viral success
• ✅ Develop Predictive Models: Create ML models to estimate virality potential
• ✅ Provide Actionable Insights: Help content creators understand virality factors

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🛠️ How the Data is Collected

The data is automatically collected using the YouTube API and stored in this GitHub location:
🔗 Trending Video Metadata

Data Collection Process

1. Fetching Trending Videos

   • Using the YouTube API, trending videos from multiple countries are retrieved.
   • The list of trending videos is stored and continuously updated (daily updates).

2. Daily Statistics Updates (Automated via GitHub Actions)

   • A scheduled GitHub Actions workflow updates video statistics (views, likes, comments, video descrition, video privacy status, etc.).
   • These updates provide historical trends for analysis.
   • The latest data is stored here:
     🔗 Video Statistics

graph TD;
    A[trending.py: Fetch Trending Videos] -->|Generates daily JSON files - one per country| B[trending_db.py: Aggregate Trending Data];
    B -->|Merges all country JSONs into a unified CSV| C[video_stats.py: Extract & Fetch Video Stats];
    C -->|Creates a daily JSON file with statistics for all videos| D[video_stats_db.py: Compile Video Stats History];
    D -->|Combines all daily stats JSONs into a final dataset| E[Complete Merged Video Stats JSON];

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📈 Defining Video Virality

Virality isn't simply measured by raw view count. Our analysis considers multiple factors, for example:

• A YouTuber with 1M subscribers getting 20M views is - potentially- expected.
• A YouTuber with 10K subscribers getting 2M views is extraordinary.

Our models will classify videos as "success" (viral) or "non-success", based on the metrics retrieved, but the success/non-success will be up to us to decide.

🔎 Key Virality Metrics

Metric	Description	Importance
Engagement Rate	Likes, comments, and shares relative to views	High
Growth Velocity	How quickly a video gains views in the first hours/days	Critical
Audience Reach	Views relative to channel subscriber count	High
Subscriber Growth	New subscribers gained after video publication	Medium
Trending Duration	How long a video remains on trending lists	Medium

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📊 Dataset & Features

Our dataset includes key video metadata and engagement statistics, such as:

• Video Details: Title, description, duration, resolution
• Engagement Metrics: Views, likes, comments, favorite count
• Channel Details: Subscriber count, total videos, upload frequency
• Trending History: How long a video remains on the trending list
• Country-Based Analysis: Virality trends across different regions

📌 Goal: Use these features to identify patterns and train models for virality prediction.

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🔬 Methodology

1️⃣ Data Collection – Retrieve daily trending videos across countries.
2️⃣ Data Cleaning & Preprocessing – Handle missing values, outliers, and standardize data.
3️⃣ Exploratory Analysis – Identify key trends and patterns.
4️⃣ Feature Engineering – Extract additional insights like growth rate and engagement score.
5️⃣ Model Development – Train ML models for virality prediction.
6️⃣ Evaluation & Interpretation – Validate predictions and refine models.

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🔨 Technologies Utilized

We've harnessed a blend of cutting-edge technologies to power the Tube Virality project:
🔹 Python 3.9 – Data processing, analysis, and ML model training. (currently) 🔹 SQL – Storing structured video metadata for analysis. (future iteration)