SQL-for-Data-Analytics VS Basketball_Analytics

Compare SQL-for-Data-Analytics vs Basketball_Analytics and see what are their differences.

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SQL-for-Data-Analytics Basketball_Analytics
1 1
252 145
0.0% -
0.0 4.1
about 1 year ago 4 months ago
Jupyter Notebook Jupyter Notebook
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

SQL-for-Data-Analytics

Posts with mentions or reviews of SQL-for-Data-Analytics. We have used some of these posts to build our list of alternatives and similar projects.

Basketball_Analytics

Posts with mentions or reviews of Basketball_Analytics. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing SQL-for-Data-Analytics and Basketball_Analytics you can also consider the following projects:

NBA-attendance-prediction - Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.

sportsipy - A free sports API written for python

nba_api - An API Client package to access the APIs for NBA.com

kepler.gl - Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets.

hoopR - An R package to quickly obtain clean and tidy men's basketball play by play data.

openbiomechanics - The open source initiative for anonymized, elite-level athletic motion capture data. Run by Driveline Baseball.