https://github.com/anuppm9917/super-store-sales-analysis-power-bi-project

My drive to know which products, regions, categories and customer segments a company should target or avoid, I search and selected an appropriate dataset on kaggle which will match a standard superstore requirement.
https://github.com/anuppm9917/super-store-sales-analysis-power-bi-project

data data-analysis data-visualization datacleansing excel exploratory-data-analysis jupyter-notebook numpy pandas plotly powerbi python3

Last synced: 11 days ago
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My drive to know which products, regions, categories and customer segments a company should target or avoid, I search and selected an appropriate dataset on kaggle which will match a standard superstore requirement.

• Host: GitHub
• URL: https://github.com/anuppm9917/super-store-sales-analysis-power-bi-project
• Owner: anuppm9917
• Created: 2024-04-14T06:52:06.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-06-06T14:38:15.000Z (8 months ago)
• Last Synced: 2024-06-06T16:15:12.200Z (8 months ago)
• Topics: data, data-analysis, data-visualization, datacleansing, excel, exploratory-data-analysis, jupyter-notebook, numpy, pandas, plotly, powerbi, python3
• Homepage:
• Size: 10.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Super-Store-Sales-Analysis-Power-BI-Project

# Overview

In pursue of knowledge and understanding of which products, regions, categories and customer segments a company should target or avoid, I search and selected an appropriate dataset on kaggle which will match a standard superstore requirement. With growing demands and cut-throat competitions in the market, companies are seeking ideas on how to optimize profits. The project is carried out in the following steps. 

# Libraries

numpy for mathematical operations on arrays.

datetime for date manipulation.

pandas to perform data manipulation and analysis.

seaborn for data visualization and exploratory data analysis.

plotly to create beautiful interactive web-based visualizations.

plotly express easy-to-use, high-level interface to Plotly.

# Languages and Tools

Python 3.11.0	Programming Language For data cleaning, manipulation and visualization Tools

# Tools & Environment	Usage

Jupyter NoteBook	An open-source IDE used to create the Jupyter document.

Power BI (Power Query, DAX)	Data visualization tool.

Kaggle	For downloading training data.

Git	A version control system to manage and keep track source code history.

# Problem Statement

Which mode of shipping is preferable?

Which customer segment is more profitable ?

Which Region makes more profit?

Which Category and sub-category makes the most sales?

Which city is preferable for business?

# Methodology

Data Collection Getting data from Kaggle.

Data Cleaning and Preparation Removing irrelevant and restructuring the dataset for easy analysis.

Exploratory Data Analysis Exploring and analyzing the cleaned data.

Visualization and Reporting visually presenting data in form of charts and graphs.

Insights presenting observations from the analysis.

# Running the project

To run the (.ipynb) project use Notebook or Google Colab, while Power BI for the (.PBIX) file.