https://github.com/jahnvisahni31/sports-popularity-forecast
This project utilizes time series forecasting techniques to predict the popularity of sports events. By analyzing historical data, we aim to provide insights into future trends and audience engagement for various sports.
https://github.com/jahnvisahni31/sports-popularity-forecast
datascience prediction-algorithm projects sports-analytics time-series
Last synced: 4 months ago
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This project utilizes time series forecasting techniques to predict the popularity of sports events. By analyzing historical data, we aim to provide insights into future trends and audience engagement for various sports.
- Host: GitHub
- URL: https://github.com/jahnvisahni31/sports-popularity-forecast
- Owner: jahnvisahni31
- Created: 2023-11-18T12:37:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-23T19:37:48.000Z (12 months ago)
- Last Synced: 2025-01-14T11:23:04.360Z (5 months ago)
- Topics: datascience, prediction-algorithm, projects, sports-analytics, time-series
- Language: Jupyter Notebook
- Homepage: https://jahnvisahni31.github.io/Sports-Popularity-Forecast/
- Size: 2.13 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Sports Popularity Forecast
📈 Time series forecasting for sports event popularity.
**Key Features:**
- Time series analysis using statistical methods.
- Predictive modeling for forecasting.
- Interactive visualizations.
**Usage:**
1. Prepare your sports viewership data.
2. Train forecasting models with provided notebooks.
3. Explore forecasted trends with visualizations.
**Dependencies:**
- Python 3.x
- Jupyter Notebooks
- NumPy, Pandas, Matplotlib, Scikit-Learn, Statsmodels
**Contributing:**
Contributions are welcome! Open issues or pull requests to improve this project.
**License:**
MIT License
[](LICENSE.md)