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Rank 3\" width=\"800\"\u003e\n\u003c/p\u003e\n\n---\n\n## Dataset\n\nData sourced from Kaggle: [NBA Games Dataset](https://www.kaggle.com/datasets/nathanlauga/nba-games)\n\n| File | Description |\n|---|---|\n| `games.csv` | Game-level results and team stats (2003–2022) |\n| `games_details.csv` | Player-level box scores per game |\n| `players.csv` | Player–team–season mapping |\n| `ranking.csv` | Daily standings per team per season |\n| `teams.csv` | Team metadata (arena, coach, GM, etc.) |\n\n---\n\n## Project Structure\n\n```\nnba-analytics-dashboard/\n│\n├── data/\n│   ├── raw/                  \n│   └── processed/           \n│\n├── notebooks/\n│   ├── 01_data_loading_and_cleaning.ipynb\n│   └── 02_feature_engineering_and_eda.ipynb\n│\n├── dashboard/                # Power BI dashboard files\n│\n├── requirements.txt\n├── LICENSE.txt\n└── README.md\n```\n\n---\n\n## Setup\n\n### 1. Clone the repository\n```bash\ngit clone https://github.com/Jayavarshini-Jayakumaran/nba-exploratory-data-analysis.git\ncd nba-exploratory-data-analysis\n```\n\n### 2. Create and activate a virtual environment\n```bash\npython -m venv venv\n\n# Windows\nvenv\\Scripts\\activate\n\n# macOS/Linux\nsource venv/bin/activate\n```\n\n### 3. Install dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 4. Add raw data\nDownload the dataset from Kaggle and place the CSV files inside `data/raw/`.\n\n### 5. Run the notebooks in order\n```\nnotebooks/01_data_loading_and_cleaning.ipynb\nnotebooks/02_feature_engineering_and_eda.ipynb\n```\n\n---\n\n## Notebooks\n\n### 01 — Data Loading \u0026 Cleaning\n- Loads all 5 raw CSVs\n- Converts `MIN` (minutes played) from `MM:SS` string to float\n- Handles nulls: fills `START_POSITION`, `COMMENT`, `NICKNAME`, `PLUS_MINUS`\n- Parses dates, engineers `GAME_RESULT` and `TOTAL_POINTS`\n- Splits `HOME_RECORD` / `ROAD_RECORD` into numeric win/loss columns\n- Exports cleaned CSVs to `data/processed/`\n\n### 02 — Feature Engineering \u0026 EDA\n- Merges game data with team metadata\n- Engineers features: `POINT_DIFF`, `WINNING_TEAM`, `HOME_WIN`, `AWAY_WIN`\n- Aggregates season-level, team-level, and player-level summaries\n- Exports final analytical tables to `data/processed/`\n\n---\n\n## Output Files (data/processed/)\n\n| File | Description |\n|---|---|\n| `games_details_cleaned.csv` | Cleaned player box scores |\n| `games_cleaned.csv` | Cleaned game results |\n| `games_with_teams.csv` | Games enriched with team metadata |\n| `games_features.csv` | Feature-engineered games table |\n| `team_stats.csv` | Per-team per-season aggregates |\n| `home_away_summary.csv` | Home vs away win rates by season |\n| `season_summary.csv` | Season-level scoring and win rate trends |\n| `player_summary.csv` | Per-player per-season averages (min 20 games) |\n| `players_cleaned.csv` | Cleaned players table |\n| `ranking_cleaned.csv` | Cleaned standings with parsed record columns |\n| `teams_cleaned.csv` | Cleaned teams metadata |\n\n---\n📧 **Email** - [jayavarshinijayakumaran11@gmail.com](mailto:jayavarshinijayakumaran11@gmail.com)\n\n🙌 **Connect** - [LinkedIn: Jayavarshini Jayakumaran](https://www.linkedin.com/in/jayavarshini-jayakumaran)\n\n📄 **License** - [MIT](LICENSE)\n\n\u003cp align=\"center\"\u003e\u003cb\u003eFinish what you started 💻\u003c/b\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayavarshini-jayakumaran%2Fnba-exploratory-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjayavarshini-jayakumaran%2Fnba-exploratory-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayavarshini-jayakumaran%2Fnba-exploratory-data-analysis/lists"}