https://github.com/ikajdan/nba_best_players_prediction

All NBA players predictions
https://github.com/ikajdan/nba_best_players_prediction

machine-learning python scrapping

Last synced: 10 days ago
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All NBA players predictions

• Host: GitHub
• URL: https://github.com/ikajdan/nba_best_players_prediction
• Owner: ikajdan
• License: mit
• Created: 2024-06-01T19:52:01.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-06-09T19:26:34.000Z (5 months ago)
• Last Synced: 2024-06-20T08:59:43.585Z (5 months ago)
• Topics: machine-learning, python, scrapping
• Language: Python
• Homepage:
• Size: 304 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# NBA Best Players Prediction

The goal of this project is to predict the best NBA players based on their statistics.

The data is scraped from the [Basketball Reference](https://www.basketball-reference.com/) website using the BeautifulSoup library. Player stats are then cleaned, preprocessed using the Pandas library, and stored in CSV files in the [data](data/) directory. Addional properties are added to the data:

- ANBA: if the player was an All-NBA player,

- RK: if the player was a rookie,

- ANBARK: if the player was an All-NBA player in their rookie year,

- Year: the year the data was collected.

The following features are available for each player:

```csv

            Player Pos  Age   Tm   G    MP   PER  ORB%  DRB%  TRB%  AST%  STL%  BLK%  USG%  OWS  DWS   WS  WS/48  OBPM  DBPM  BPM  VORP  ANBA  RK  ANBARK  Year

0        Ömer Aşık   C   31  TOT  18   182   3.1   5.5  22.2  14.0   2.1   0.5   1.9   8.7 -0.2  0.2 -0.1 -0.021  -6.5  -1.5 -8.0  -0.3     0   0       0  2018

1     Luke Babbitt  SF   28  TOT  50   715   9.8   1.1  13.9   7.5   6.5   0.5   0.8  15.0  0.5  0.4  0.9  0.060  -0.9  -1.5 -2.4  -0.1     0   0       0  2018

2  Marco Belinelli  SG   31  TOT  80  1947  14.1   1.1   7.3   4.3  11.9   1.6   0.5  20.7  2.4  1.3  3.7  0.092   0.4  -0.8 -0.5   0.8     0   0       0  2018

```

The data is then used to train a Random Forest model using the Scikit-learn library.

Only a select number of features are used to train the model. Those were selected based on their importance in predicting the best players.






  

  



  Feature importances of each feature in the Random Forest model.






Based on the that information, the following features were selected:

```python

selected_features = [

    "VORP",

    "WS",

    "OWS",

    "BPM",

    "OBPM",

    "USG%",

    "PER",

    "DWS",

    "MP",

    "WS/48",

]

```

After training the model, the best players are predicted based on the data from the 2024 season. The results are stored in the JSON file.

## Usage

To run the project, first install the required dependencies:

```python

pip install -r requirements.txt

```

Then run the following command:

```python

python Kajdan_Ignacy.py output.json

```

## License

This repository is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.