https://github.com/sehgal-vishal/python-superstoresales-project

This is my python superstore sales data analysis project. I have done exploratory data analysis with some visualization. For visualization i used matplotlib and plotly express and i have also done some predictive analysis.
https://github.com/sehgal-vishal/python-superstoresales-project

Last synced: 4 days ago
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This is my python superstore sales data analysis project. I have done exploratory data analysis with some visualization. For visualization i used matplotlib and plotly express and i have also done some predictive analysis.

• Host: GitHub
• URL: https://github.com/sehgal-vishal/python-superstoresales-project
• Owner: sehgal-vishal
• Created: 2023-07-07T11:48:24.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-22T13:43:11.000Z (5 months ago)
• Last Synced: 2024-06-22T21:52:40.957Z (5 months ago)
• Language: Jupyter Notebook
• Size: 5.82 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# python-superstoresales-project

This is python superstore sales data analysis project. The Python Superstore Sales project involved conducting exploratory analysis and visualization using Plotly Express to analyze sales data from a fictional superstore. Through thorough data exploration and preprocessing, key insights were uncovered regarding the sales performance of the superstore. Utilizing Plotly Express, a powerful Python library for visualization, various interactive and visually appealing charts were created, such as bar charts, line plots, and scatter plots. These visualizations effectively showcased sales patterns, identified top-selling product categories, high-performing regions, and customer segments with significant purchasing power. The project outcomes provide actionable insights for optimizing sales strategies and improving overall performance.

1. When we find the sales by category we got to know that office supplies is the most sold category and furniture is the least sold.

2. By sub category phones are the most sold and fasteners are the least sold.

3. By segment analysis our highest profit was from consumer and least from home office.

4. Our product return rate was 0.163 and return cost was 287.

5. By category our highest revenue was from technology and least was from furniture.