https://github.com/busyyang/nba_winner_prediction

https://github.com/busyyang/nba_winner_prediction

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/busyyang/nba_winner_prediction
• Owner: busyyang
• License: mit
• Created: 2019-12-09T03:06:57.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-10T10:50:38.000Z (about 5 years ago)
• Last Synced: 2023-10-19T21:08:17.232Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 220 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        
# NBA winner prediction

## dataset

The data set is downloaded from [basketball-reference.com](https://www.basketball-reference.com/). Choose `Seasons` and pick a season such as `2018-19`. Get `Team Per Game Stats`, `Opponent Per Game Stats` and `Miscellaneous Stats`, and save them as `cvs` file in `data` subpath.

Just like 

 

 ![data files](./images/data_file.png)

 

then the features can be create using those files. And match result label information can be got from `Schedule and Results`(as the same as studima200 offered). Save all results into `csv` file.

Please check './data/Year_2016_2017.csv', './data/Year_2017_2018.csv' and './data/Year_2018_2019.csv'.

Check `./data` subpath is like:

![all_files](./images/all_files.png)

 

## model

Machine Learning method: Tecision Tree, Random Forest, XGBoost, Logistic Regression and Naive Bayes (Gaussian) are programmed in `studima200Project.py`. 

~~~python

python ./NBA_prediction.py

~~~

And an `.ipynb` file is offered to run this python script step by step. More modification sugesstions (shift feature file and label file) are also noted in that `.ipynb` file.

## result

result should be like:

![result](./images/result.png)

Please note: Since data sets are shuffled randomly, and random process is also included in model trainning process, the result may be different in every trial.

## requirements

sklearn\

pandas\

numpy\

xgboost

## file history

|Date|Version|Comments|

|:--:|:--:|:--:|

|2019/12/9|V0.1|Init|

## ref

more detail:

https://blog.csdn.net/moy37rqw1jarn33bgzk/article/details/80602924