https://github.com/katrinafermanto23/nba-trends

https://github.com/katrinafermanto23/nba-trends

jupyter-notebook matplotlib pandas python

Last synced: 12 days ago
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• Host: GitHub
• URL: https://github.com/katrinafermanto23/nba-trends
• Owner: Katrinafermanto23
• Created: 2025-01-14T09:28:06.000Z (29 days ago)
• Default Branch: main
• Last Pushed: 2025-01-23T06:12:16.000Z (20 days ago)
• Last Synced: 2025-01-23T07:22:39.255Z (20 days ago)
• Topics: jupyter-notebook, matplotlib, pandas, python
• Language: Python
• Homepage:
• Size: 4.83 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
NBA Trends Analysis

This project analyzes a subset of NBA data, focusing on 5 teams and key statistics.

Data: The dataset is derived from 538's Analysis of the Complete History of the NBA, utilizing original Basketball Reference data with additional variables from 538. This project focuses on a limited set of 5 teams and 10 columns, including points scored, points allowed, and other relevant statistics.

**Project Goals:** 

Exploratory Data Analysis:

  - Visualize key trends and relationships within the data using various charts and tables.

  - Investigate potential associations between different variables (e.g., points scored vs. wins).

Data Insights:

  - Discover interesting patterns and trends in the performance of the selected NBA teams.

  - Gain a deeper understanding of the factors that contribute to team success.

Key Features:

  - Utilize libraries like Matplotlib or Seaborn to create informative and visually appealing charts (e.g., scatter plots, line graphs, bar charts).

  - Generate clear and concise tables to summarize key statistics.

Data Analysis:

  - Perform basic statistical analysis to identify potential correlations and relationships.

  - Draw meaningful conclusions based on the observed trends.

**Technologies Used:**

  - Python

  - Pandas

  - Matplotlib/Seaborn (or other visualization libraries)