https://github.com/quantumudit/regional-sales-analysis
This project focuses on analyzing and visualizing the United States regional sales for a fictitious company in between 2018-2020 using Python & Power BI.
https://github.com/quantumudit/regional-sales-analysis
data-analysis data-visualization databases jupyter-notebook power-bi python sqlite
Last synced: 5 months ago
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This project focuses on analyzing and visualizing the United States regional sales for a fictitious company in between 2018-2020 using Python & Power BI.
- Host: GitHub
- URL: https://github.com/quantumudit/regional-sales-analysis
- Owner: quantumudit
- License: other
- Created: 2021-12-18T18:45:44.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-12-20T19:33:18.000Z (almost 4 years ago)
- Last Synced: 2025-02-17T10:49:42.343Z (8 months ago)
- Topics: data-analysis, data-visualization, databases, jupyter-notebook, power-bi, python, sqlite
- Language: Python
- Homepage:
- Size: 1.55 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 & Visualizing Regional Sales across the United States with Python and Power BI
Overview •
Prerequisites •
Architecture •
Demo •
Support •
License
## Overview
This project focuses on analyzing and visualizing the regional sales across the United States in between 2018-2020. The datasets used are completely fictitious and solely made-up just for data analysis case study.
The repository directory structure is as follows:
Regional-Sales-Analysis
├─ 01_SOURCE
├─ 02_ETL
├─ 03_DATA
├─ 04_ANALYSIS
├─ 05_DASHBOARD
├─ 06_RESOURCES
The type of content present in the directories is as follows:
**01_SOURCE**
This directory contains the the received/downloaded raw data that needs to be cleaned and organized to ease out the data analysis and visualization process.
**02_ETL**
This directory contains the ETL script that takes the raw dataset(s) as input, transforms it and exports an analysis-ready dataset into the _03_DATA_ directory.
In this project; we've exported the clean datasets in the form of comma separated flat files into the _FLATFILES_ folder and in the form of SQLite database into the _DATABASE_ folder.
**03_DATA**
This directory contains the data that can be directly used for exploratory data analysis and data visualization purposes.
In this project; we have two sub-folders under the _03_DATA_ folder that holds the following:
- _FLATFILES_: comma separated flat files
- _DATABASE_: SQLite Database
Both folders has the exact same data but in two different format.
**04_ANALYSIS**
This directory contains the python notebooks that analyzes the clean dataset to generate insights.
For analyzing the data with Jupyter Notebook; we have used the clean dataset present in the SQLite database.
**05_DASHBOARD**
This directory contains the markdown file with an embedded Power BI report link 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.
**06_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 & Jupyter Notebook
- 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][requirements] 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 shown in the above workflow; we are first performing necessary cleaning and transformation in the received raw dataset using Python and exporting the clean dataset as comma-separated flat files and also as a SQLite database.
Finally; we leverage the clean & analysis-ready dataset for exploratory data analysis (EDA) using Jupyter Notebook and creating an insightful report using Power BI.
## Demo
The interactive Power BI dashboard can be viewed here:
[![Power BI Dashboard][dashboard_image]][dashboard_link]
## 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]: 06_RESOURCES/project_cover_image.png
[process_workflow]: 06_RESOURCES/process_architecture.png
[scraping_graphic]: 06_RESOURCES/scraping_graphic.gif
[dashboard_image]: 06_RESOURCES/dashboard_image.png
[requirements]: ./requirements.txt
[linkedin]: https://www.linkedin.com/in/uditkumarchatterjee/
[twitter]: https://twitter.com/quantumudit
[dashboard_link]: https://app.powerbi.com/view?r=eyJrIjoiYzJiNWRkOWUtYzFmNi00NzVmLTg0NWMtZTljZWY4MmQwZmZlIiwidCI6IjcwODlkNGIxLTQyMmUtNDYzZi1hNGM3LTViY2FiOTk0MGRiZCJ9&pageName=ReportSection8b0879d590be87cd63d7
[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