TikTok View Predictor
A machine learning-powered model that predicts the number of views a TikTok video will receive using linear regression and data analytics.
Overview
TikTok View Predictor is an innovative machine learning project that forecasts the number of views a TikTok video can receive. By leveraging linear regression models and data processing techniques, the pipeline ingests historical video data, cleans and analyzes it, and finally predicts future view counts. This insight-driven approach assists content creators and marketers in optimizing their strategies.
Key Features
Predictive Analytics
Uses linear regression and statistical analysis to accurately forecast TikTok video views.
Data Pipeline Integration
Automates data extraction from TikTok APIs and CSV datasets, transforming raw data into actionable insights.
Interactive Dashboard
Visualizes predictions and performance metrics, enabling users to explore trends and refine content strategies.
Technical Details
Data Sources & Extraction
- API integration with TikTok data endpoints
- CSV ingestion for historical video performance data
Software Stack
- Python for data processing & model building
- Flask for serving APIs and web integration
- Scikit-learn for regression modeling
- Jupyter Notebook for data exploration & analysis
- Numpy and Pandas for data manipulation
this is the predicted graph for future tiktok views
Challenges I Encountered
One major challenge was cleaning diverse data formats from multiple sources and ensuring the regression model was robust against outliers. We overcame these challenges by employing advanced data preprocessing techniques and continuous model evaluation.
Accomplishments
The TikTok View Predictor achieved strong forecasting performance, providing content creators with reliable estimates for video views. The end-to-end pipeline streamlined data collection, analysis, and visualization, enhancing decision-making and content optimization strategies.
What's Next for TikTok View Predictor
Future improvements include incorporating more advanced machine learning models, expanding data source integration, and developing automated reporting capabilities to further empower content strategy optimizations.
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