TikTok Engagement Analytics - Content Performance & Trends

This repository contains my work for the TikTok capstone project from the Google Advanced Data Analytics programme. The goal is to use TikTok post-level data to:

• Explore which content features (e.g. video category, duration, hashtags, posting time) are associated with higher engagement.
• Visualise engagement patterns and trends.
• Provide practical recommendations for improving content performance.

The analysis is implemented in Python using pandas, NumPy, Matplotlib, Seaborn, Plotly, and SciPy.

---

Repository structure

• Tik_Tok_Google_Advanced_Data_Analytics.ipynb
  Main Jupyter notebook. It includes:
  • Data loading and cleaning for the TikTok dataset.
  • Exploratory data analysis (EDA) with summary statistics.
  • Visualisations of engagement metrics (likes, shares, comments, views).
  • Feature analysis by category/hashtag/posting time/etc.
  • Simple statistical tests and/or correlations to quantify relationships.
• tiktok_dataset.csv
  The TikTok dataset used in the analysis, containing post-level features and engagement metrics.
• README.md
  This file.

---

How to run the notebook

1. Clone the repository

git clone https://github.com/barbavegeta/Google_Advanced_Data_Analytics-TikTok_Project.git
cd Google_Advanced_Data_Analytics-TikTok_Project

2. Create and activate an environment

Using conda (example):

conda create -n tiktok_project python=3.10 -y
conda activate tiktok_project

3. Install dependencies

pip install   numpy   pandas   matplotlib   seaborn   plotly   scipy   jupyter

(If some extra library is used in the notebook and missing, install it when Python complains.)

4. Launch Jupyter

jupyter notebook

Open:

Tik_Tok_Google_Advanced_Data_Analytics.ipynb

and run the cells from top to bottom.

Analysis outline (high level)

The notebook roughly follows these steps:

1. Load & inspect data
   • Read tiktok_dataset.csv into a pandas DataFrame.
   • Inspect column names, data types, and basic structure.
   • Check for missing values and obvious data quality issues.
2. Exploratory data analysis (EDA)
   • Compute summary statistics for engagement metrics (views, likes, comments, shares).
   • Visualise distributions and identify outliers.
   • Look at engagement broken down by key features (category, content type, etc.).
3. Feature relationships & statistical checks
   • Examine correlations between numeric features and engagement.
   • Compare engagement across categories/segments using groupby operations and simple tests where relevant (e.g. t-tests/ANOVA-style checks).
4. Visualisation
   • Build static plots (Matplotlib/Seaborn) and, optionally, interactive charts (Plotly) to show:
     • Top-performing content categories.
     • Engagement vs posting time or other relevant features.
     • Relationships between multiple engagement metrics.
5. Recommendations
   • Translate the patterns into concrete, data-backed suggestions for optimising TikTok content: e.g. what to post more of, when to post, which metrics to track.

---

Notes

• This project is a learning / portfolio piece based on a teaching dataset, not a production system.
• The emphasis is on:
  • Clean exploratory analysis.
  • Clear visualisations.
  • Direct links from data patterns to practical content strategy recommendations.