NBA Data Analysis Project

Introduction

This project is dedicated to analyzing NBA data spanning several seasons to derive insightful visualizations and statistics. It utilizes Jupyter notebooks for data exploration and analysis, with a focus on cumulative player statistics and team-specific data. The project aims to leverage Python's powerful data manipulation and visualization libraries to uncover trends and patterns in NBA gameplay.

Getting Started

Prerequisites

Python 3.6+
pip

Installation

Clone the repository:

git clone https://github.com/nprasad2077/jupyter-nba.git
cd jupyter-nba

Setup a virtual environment

python -m venv venv

Activate Virtual Environment:

Unix or MacOS: source venv/bin/activate
Windows: .\venv\Scripts\activate

Install required packages:

pip install -r requirements.txt

Launch Jupyter Notebooks:

jupyter notebook

Navigate to the 'notebook' directory and open a notebook of your choice.

Project Structure

data/: Contains datasets used for analysis. notebooks/: Jupyter notebooks for various analyses, including individual player statistics and team data. output/:

html/: HTML outputs of the notebooks for easy sharing and viewing.
images/: Graphs and plots exported as images from the analyses.
pdf/: PDF exports of the notebooks for documentation and offline viewing. scripts/: Python scripts used for data fetching and preprocessing. visualizations/: Directory intended for additional visualization scripts or outputs. LICENSE: Details on the usage and distribution of the project. README.md: This document, explaining the project setup, usage, and contribution guidelines. requirements.txt: Required Python packages for running the project.

Licensing

This project is licensed under the terms of the GNU GENERAL PUBLIC LICENSE.