nba_analytics

Compiling a report is the primary goal of this project. It is located in the REPORT.md file. This is a project for organizing data collection, data processing, and machine learning tasks related to NBA player statistics, specifically to determine valuable players among the DETROIT PISTONS. Another portion of this project is performing the same, local functions through cloud architecture (Azure SQL, Azure Blob Storage, Databricks, Azure Synapse, and PowerBI).

Architecture Comparison of Cloud vs. Local Data Engineering/Analytics:

Usage

To use this project, clone the repository and set up the necessary dependencies. Create an environment (Ctrl+Shift+P on VSCODE) using the requirements.txt. You can then run the scripts in the main_ipynb.ipynb for easy use or directly in the src directory for data collection, processing, and machine learning tasks.

Directory Structure

The project directory is organized as follows:

• data/: Contains datasets used in the project.

  • datasets/
  • nba_players.csv: Dataset containing information about NBA players.
  • nba_player_stats_5years_overlap.csv: Dataset containing every 5 consecutive years of NBA player statistics (from nba_player_stats_5years.csv).
  • nba_player_stats_5years_tensor_ready.csv: PyTorch import version of nba_player_stats_5years.csv.
  • nba_player_stats_5years.csv: Dataset (csv) containing first 5 years of NBA player statistics.
  • nba_player_stats_5years.json: Json version of nba_player_stats_5years.csv.
  • nba_players_advanced.csv: Dataset containing advanced NBA player statistics.
  • nba_players_basic.csv: Dataset containing basic NBA player statistics.
  • nba_player_stats.csv: Dataset containing combined NBA player statistics.
  • graphs: Contains data analytic graphs from analytics/.
  • models: Contains machine learning models from machine_learning/.
  • reports: Location for PowerBI and local pdf created reports from src/utils/reporting.py.

• logs/: Contains log files generated during the project.

  • nba_player_stats.log: Log file for NBA player statistics data processing.

  • src/: Contains the source code for data collection, data processing, and machine learning tasks.

  • dataset/: Contains scripts for processing and cleaning data.

    • creation.py: Module for creating datasets from NBA API using basketball_reference_web_scraper.
    • processing.py: Module for processing datasets to create a useful dataset.
    • torch.py: Module for processing datasets for PyTorch/machine learning evaluation.
    • filtering.py: Module for processing datasets further (possibly to be used by dataset_processing.py).

  • machine_learning/: Contains scripts for machine learning tasks.

    • models/: Contains models to be used for the machine learning tasks.
    • arima.py: (TODO for better step evaluation)
    • lstm.py: LSTM neural networks (custom and PyTorch built-in) for Many-to-Many prediction.
    • neuralnet.py: Basic neural net for 1-to-1 prediction
    • train_models.py: Module for directly training models in models/.
    • use_models.py: Module for directly using models in models/.

  • utils/: Contains utility scripts used across the project.

  • logger.py: Utility script for logging messages.

  • config.py: Utility for settings among files.

• generate_requirements.bat: Batch file to generate the requirements.txt file.

• requirements.txt: File containing project dependencies.

• reference: Any other files related to the project used for referencing.

Work Schedule (will be moved to project Github when complete)

Contributors

• Alexander Hernandez

Feel free to contribute to this project by submitting pull requests or opening issues.