TikTok Data Analysis Project

Introduction

Welcome to my exploration of TikTok data! This project delves into the fascinating world of short-form video content to discover patterns and insights using data science and visualization techniques.

Project Overview

TikTok has emerged as a cultural phenomenon, capturing the attention of millions with its short and engaging video content. In this project, I analyzed a dataset containing information about TikTok videos, focusing on metrics such as view counts, likes, shares, comments, and the impact of author status on engagement.

Data Source

The dataset for this analysis includes various engagement metrics and author statuses for TikTok videos and can be derived from a publicly available source that includes details about the titles available on TikTok from Kaggle Website.

Tools Used

• Python: Language
• Pandas, Matplotlib and Seaborn: Libraries
• Jupyter Notebook: The environment for running Python code and presenting the analysis.

Key Findings

• A nearly balanced distribution of 'claim' and 'opinion' videos, suggesting a varied content strategy on the platform.
• Higher engagement metrics (views, likes, shares, downloads, comments) for videos from banned or under-review authors, indicating that such videos may draw more attention, potentially due to their controversial nature.
• No significant correlation between video duration and engagement metrics, implying that content quality or other factors might play a more pivotal role in user engagement.

Challenges Faced

I encountered challenges around data cleaning, particularly in handling missing values (whether to replace with 'unknown' or delete missing value records), and mastering the art of visual data storytelling. Each hurdle presented a learning curve, enhancing my data wrangling and analytical skills.

Conclusion

This project has provided me with a deeper understanding of user engagement on TikTok and has honed my analytical abilities. It's a step forward in my journey to becoming adept at uncovering the narratives data can tell.

Future Work / Area of Improvements / If additional relevant datasets become available

• Analyzing text data from video transcriptions to understand content themes and their impact on engagement.
• Investigating the effect of video features (like filters and music) on a video's popularity.
• Studying user behavior patterns, such as peak usage times and content preferences, to better understand the TikTok ecosystem.

Thank you for visiting my project. Feel free to reach out and/or comment on my work. :)