Tiktok Analysis | Panther's Portfolio

📍 Project Overview

This project aimed to analyze key factors influencing video virality on TikTok using data analytics and machine learning. By studying engagement patterns and content characteristics, I developed a model to predict video performance.

📈 Key Outcomes

🚀 "Claim" videos received higher engagement, suggesting informational content performs better on TikTok.
📊 Likes, shares, and downloads strongly correlate, emphasizing engagement as a predictor of virality.
🎥 Verified status had minimal impact, highlighting that content quality outweighs creator credibility.
📈 Minimal correlation between video duration and virality, emphasizing content quality over length.

🚀 Solution

✅ Descriptive & Correlation Analysis – Explored relationships between video duration, likes, shares, and engagement metrics.
✅ A/B Testing – Assessed the impact of content type ("claim" vs. "opinion") on viewership trends.
✅ Text & Sentiment Analysis – Identified key patterns in video transcripts that correlate with higher engagement.
✅ Clustering & Machine Learning – Used K-means clustering to categorize viral content characteristics.
✅ Strategic Recommendations – Provided insights on content optimization, platform trends, and engagement tactics for maximizing video visibility.

TikTok Video Virality Analysis

📍 Project Overview

📈 Key Outcomes

⚡ Problem Statement

🚀 Solution

🔍 Tools & Methods Used

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