https://github.com/aneripatel28/gamedaypredictornba
Science Score: 13.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (12.6%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: AneriPatel28
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 1.34 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
NBA Games Analysis
Project Overview
This project conducts an extensive analysis of NBA games, leveraging data from Nathan Lauga's NBA dataset on Kaggle. The study utilizes machine learning to predict game outcomes and analyze player performances across multiple seasons. This analysis is organized into three Jupyter notebooks and a comprehensive report, each detailing different facets of the NBA data.
Objectives
- Exploratory Data Analysis: To uncover key trends and patterns in player performances and game outcomes.
- Classification Models: To predict the likelihood of a home team's victory.
- Regression Models: To forecast the home team's total score based on various game statistics.
- Report Generation: To provide insights and a detailed methodology of the analysis.
Technologies Used
- Python
- Jupyter Notebook
- Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn
Features
- Detailed statistical analysis of game and player data.
- Predictive modeling for game outcomes and scoring.
- Visual representations of data trends and predictions.
Installation
Clone this repository to your local machine:
bash
git clone https://github.com/your-username/NBAGamesAnalysis.git
cd NBAGamesAnalysis
Install required Python packages:
bash
pip install notebook pandas numpy scikit-learn matplotlib seaborn
Usage
Start Jupyter Notebook and access the analysis notebooks:
bash
jupyter notebook
Open and run cells in the provided .ipynb files to replicate the analysis and view results.
Contributing
Contributions to enhance the analysis or extend the scope of the project are welcome. Please fork the repository and submit a pull request with your changes.
License
This project is released under the MIT License. See the LICENSE file in the repository for more details.
Owner
- Login: AneriPatel28
- Kind: user
- Repositories: 1
- Profile: https://github.com/AneriPatel28