This project focuses on analyzing and predicting NBA MVP candidates based on player statistics and team performance. It includes data analysis techniques to identify top performers and trends over different eras.
nba-mvp-prediction/
├── data/ # Directory for datasets
│ └── NBA_Dataset.csv # NBA dataset used for the analysis
├── src/ # Source code directory
│ └── mvp_prediction.py # Main script for MVP prediction
├── requirements.txt # Dependencies required for the project
├── README.md # Project description and instructions
└── .gitignore # Files to ignore (e.g., pycache/, .DS_Store)
The dataset used for this project contains various statistics for NBA players, including points per game, assists per game, rebounds per game, and Player Efficiency Rating (PER). The data is stored in the data/NBA_Dataset.csv file.
Columns:
player: The name of the player.pts_per_g: Points scored per game.ast_per_g: Assists per game.trb_per_g: Rebounds per game.per: Player Efficiency Rating.team_id: Identifier for the team.
- Python 3.7 or higher
- The following Python libraries:
- pandas
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Clone the repository:
git clone https://github.com/yourusername/nba-mvp-prediction.git cd nba-mvp-prediction -
Install the required packages: bash pip install -r requirements.txt
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Place the NBA_Dataset.csv file in the data/ directory.
1 Navigate to the src/ directory:
bash
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cd src
2. Run the mvp_prediction.py script:
bash
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python mvp_prediction.py
This script will perform the following steps: Load and analyze the NBA dataset. Compare player statistics and identify top performers. Provide insights on MVP candidates and team championships.
The current version includes basic analysis with player statistics comparisons. Future improvements may include implementing predictive models and expanding data analysis.
Contributions are welcome! Please feel free to submit a pull request or open an issue for suggestions.
This project is licensed under the MIT License. See the LICENSE file for details.