amy-panda/nba_career_prediction

Predicting if a NBA rookie player will last at least 5 years in the league

NBA Career Prediction

Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, this project undertook data processing and developed multiple classification models to forecast whether a rookie player would continue playing in the NBA league for at least five years. These models encompassed Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, AdaBoost, and XGBoost. The top-performing classifiers, Logistic Regression and XGBoost, were identified based on key performance metrics, including ROC-AUC scores and Confusion matrix.

🤝 Contributors

• Amy Yang
• Chanthru Vimalasri
• Yatindra Vegunta

🗼 Project Organization

├── README.md          <- README file with project details.
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├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- Including training and validation sets.
│   └── raw            <- Including 2022_train.csv and 2022_test.csv files.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Including the data preprocessing and two best models. 
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
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└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── data           <- Scripts to download or generate data
    │   └── sets.py  
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
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    └── models         <- Scripts to train models and then use trained models to make
        │                 predictions
        ├── null.py
        └── performance.py

Note: The project organisation above is adapted with the cookiecutter data science project template.

🛠 Tools and Techniques

• Feature engineering
• Imputation methods such as single imputation by using mean/median, multiple imputation and Nearest neighbour imputation
• Imbalance data treatment including oversampling, undersampling, STOME and hyperparameter setting
• Model training with the packages including lazypredict and scikit-learn
• Hyperparameter tuning with random search, grid search and automatic search using the Hyperopt package
• Model evaluation with ROC-AUC score and Confusion Matrix plot

ℹ️ Data Source

Kaggle Competition [UTS AdvDSI 2022-11] NBA Career Prediction