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- Develop a dataset for a machine learning model to classify video content as claims or opinions.
- Analyze engagement trends to determine factors that influence classification.
- Enhance content moderation using AI-driven classification models to reduce manual review workload.
✅ Cleaned and structured dataset, focusing on key text-based and engagement variables.
✅ Minimum and maximum value analysis conducted for selected variables.
✅ New feature engineering completed, including word frequency analysis for predictive modeling.
✅ Irrelevant columns removed to optimize model performance and data processing speed.
✅ Deploy Model for Content Moderation – Implement AI-powered automation to assist TikTok’s content review teams.
This project lays the foundation for automated claims classification on TikTok, improving content moderation efficiency and accuracy. By leveraging machine learning and AI, TikTok can enhance user experience, reduce misinformation, and streamline claim verification processes.
- 📜 PACE_TIKTOK
- 📘 TIKTOK_JPYNB)
- 📈 Visual Reports:
To fully interact with the Power BI visualizations, download and install Power BI Desktop:
- Download Power BI Desktop
- Open the
.pbixfile using Power BI Desktop. - Explore interactive dashboards, filters, and insights!
🔹 Download Power BI Files:
- SUMS_TikTok_Dashboard.pbix
- TikTok_Visual_Table_Report_Dashboard.pbix
- TikTok_Watchtime_Dashboard.pbix
📁 Data – Cleaned datasets for machine learning training.
📁 Notebooks – Jupyter notebooks with data preprocessing, analysis, and modeling.
📁 Models – Saved trained models and performance evaluations.
📁 Visualizations – Charts and graphs illustrating key insights.
📜 README.md – Overview of the project, key insights, and next steps.
👩💻 Author: Sulay Cay
🔗 LinkedIn: linkedin.com/in/sulay-cay-0589513a
💻 GitHub: github.com/sulay01
📅 Date: February 10, 2025
Data is fictional and completed as a demonstration solely for this project for Google.
RESOURCES: GOOGLE DATA ADVANCED ANALYTICS