https://github.com/quantumudit/basketball-players-analysis
The project focuses on analyzing salaries and various other in-game metrics of top NBA basketball players from 2005-14 by performing exploratory data analysis with Python and Jupyter Notebook and by visualizing the data in an insightful dashboard made with Power BI
https://github.com/quantumudit/basketball-players-analysis
data-analysis jupyter-notebook power-bi python
Last synced: 3 months ago
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The project focuses on analyzing salaries and various other in-game metrics of top NBA basketball players from 2005-14 by performing exploratory data analysis with Python and Jupyter Notebook and by visualizing the data in an insightful dashboard made with Power BI
- Host: GitHub
- URL: https://github.com/quantumudit/basketball-players-analysis
- Owner: quantumudit
- License: other
- Created: 2021-11-28T05:58:07.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-11-28T05:59:10.000Z (almost 4 years ago)
- Last Synced: 2024-12-26T08:42:27.298Z (10 months ago)
- Topics: data-analysis, jupyter-notebook, power-bi, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.18 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
![Project Logo][project_logo]
---
Analyzing salary and in-game metrics of top-10 highest paid NBA basketball players from 2005-2014 with Python and Power BI
Overview •
Prerequisites •
Architecture •
Demo •
Support •
License
## Overview
This project focuses on analyzing salaries and various other in-game metrics of top NBA basketball players by performing exploratory data analysis to generate insights and visualizing the data with the help of Power BI.
The repository directory structure is as follows:
Basketball-Players-Analysis
├─ 01_ETL
├─ 02_DATA
├─ 03_ANALYSIS
├─ 04_DASHBOARD
├─ 05_RESOURCES
The type of content present in the directories is as follows:
**01_ETL**
This directory contains the Python raw data file and Python ETL script that takes numpy matrix in the raw data file as input, transforms it and exports an analysis-ready dataset into the _02_DATA_ directory.
**02_DATA**
This directory contains the data that can be directly used for exploratory data analysis and data visualization purposes.
**03_ANALYSIS**
This directory contains the python notebooks that analyzes the clean dataset to generate insights
**04_DASHBOARD**
This directory contains the python notebook with an embedded Power BI report that visualizes the data. The Power BI dashboard contains slicers, cross-filtering and other advance capabilities that end user can play with to visualize a specific facet of the data or, to get additional insights.
**05_RESOURCES**
This directory contains images, icons, layouts, etc. that are used in this project
## Prerequisites
The major skills that are required as prerequisite to fully understand this project are as follows:
- Basics of Python
- Python libraries: Numpy, Pandas, Matplotlib
- Basics of Python Notebooks
- Basics of Power BI
In order to complete the project, I've used the following applications and libraries
- Python
- Python libraries mentioned in requirements.txt file
- Jupyter Notebook
- Visual Studio Code
- Microsoft Power BI
> The choice of applications & their installation might vary based on individual preferences & system settings.
## Architecture
The project architecture is quite straight forward and can be explained through the below image:
![Process Architecture][process_workflow]
As per the above workflow; we are first importing data present as numpy matrix from the Python file which is then processed and cleaned with another ETL specific Python script.
Finally; we leverage the clean & analysis-ready dataset for some exploratory data analysis (EDA) using Jupyter Notebook and creating an insightful report using Power BI
## Demo
To be updated.
## Support
If you have any doubts, queries or, suggestions then, please connect with me in any of the following platforms:
[![Linkedin Badge][linkedinbadge]][linkedin] [![Twitter Badge][twitterbadge]][twitter]
If you like my work then, you may support me at Patreon:
## License
This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.
[project_logo]: 05_RESOURCES/project_cover_image.png
[process_workflow]: 05_RESOURCES/process_architecture.png
[scraping_graphic]: 05_RESOURCES/scraping_graphic.gif
[website_link]: https://www.mohfw.gov.in/
[linkedin]: https://www.linkedin.com/in/uditkumarchatterjee/
[twitter]: https://twitter.com/quantumudit
[linkedinbadge]: https://img.shields.io/badge/-uditkumarchatterjee-0e76a8?style=flat&labelColor=0e76a8&logo=linkedin&logoColor=white
[twitterbadge]: https://img.shields.io/badge/-@quantumudit-1ca0f1?style=flat&labelColor=1ca0f1&logo=twitter&logoColor=white&link=https://twitter.com/quantumudit