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Statistical analysis of potential association between a NBA team’s number and type of injuries to their record from the 2010-15 seasons. Prediction of 2016 season records given injury types and numbers.

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Effect of NBA Injuries on Team Record

View the notebook here: https://connormcmanigal.github.io/Data-Analysis-EffectofNBAInjuriesonTeamRecord/final_report.pdf

Overview: This notebook is a project from my Data Science in Practice (COGS108) course from UC San Diego. You can access the project and its contents by viewing the "COGS108FinalProject.ipynb". Within the notebook we conducted descriptive, exploratory, and inferential analysis on two data sets that we located on Kaggle: one on injury stats and another on team records. We set out with the goal of analyzing the relationship between the number of injured players on a given NBA team and their overall record. Our code is written in python and we utilized many libraries including matplotlib, pandas, numpy, sklearn, and seaborn. I hope you enjoy!

Screenshot 2024-06-02 at 9 07 35 PM

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Statistical analysis of potential association between a NBA team’s number and type of injuries to their record from the 2010-15 seasons. Prediction of 2016 season records given injury types and numbers.

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