https://github.com/isha4001/e-commerce_sales_analysis

Analyze an E-Commerce sales data with Python, Pandas, and Plotly to uncover profit making category, and sales trends. Includes data cleaning, insights, and visualizations. Tech Stack: Python | Pandas | Plotly | Jupyter.
https://github.com/isha4001/e-commerce_sales_analysis

dataanalysis dataanalysis-projects ecommerceproject pandas plotly python

Last synced: 10 months ago
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Analyze an E-Commerce sales data with Python, Pandas, and Plotly to uncover profit making category, and sales trends. Includes data cleaning, insights, and visualizations. Tech Stack: Python | Pandas | Plotly | Jupyter.

• Host: GitHub
• URL: https://github.com/isha4001/e-commerce_sales_analysis
• Owner: Isha4001
• Created: 2025-02-10T13:55:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-11T05:08:29.000Z (about 1 year ago)
• Last Synced: 2025-04-05T18:13:29.711Z (10 months ago)
• Topics: dataanalysis, dataanalysis-projects, ecommerceproject, pandas, plotly, python
• Language: Jupyter Notebook
• Homepage:
• Size: 5.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# E-Commerce_Sales_Analysis

## PROJECT OBJECTIVE

To conduct a comprehensive analysis of an E-commerce company sales, and product trends using various libraries in Python to identify key insights and opportunities for business optimization.

**Tech Stack**: Python | Pandas | Plotly | Jupyter.

**Key Insights**: Monthly sales and profit based on various factors, best-selling products and product category, Sales to profit ratio.

## QUESTIONS (KPIs)

1. Calculate the monthly sales of the store and identify which month had the highest sales and which month had the lowest sales.

2. Analyze sales based on product categories and determine which category has the lowest sales and which category has the highest sales.

3. Sales analysis need to be done based on sub categories.

4. Analyze the monthly profit from sales and determine which month had the highest profit.

5. Analyze the profit by category and sub-category.

6. Analyze the sales and profit by customer segment.

7. Analyze the sales to profit ratio.

_Since this code uses plotly, please open the jupyter notebook in nbviewer._

## DATASET USED

- Dataset

## PROCESS

* Verify data for any null values and anomalies

* Make sure data is consistent and clean with respect to data type, data format and values used.

* Create suitable charts with the help of pandas and plotly in jupyter notebook.

* Merge all conclusion and give overall result.

## JUPYTER NOTEBOOK

- Jupyter Code

## PROJECT INSIGHT

  * November recorded the highest sales, while February had the lowest.

  * The Technology category emerged as the best-selling segment

  * Phones and Chairs were the top-selling products.

  * December saw the highest profit, whereas January had the lowest

  * The Technology category contributed the most to overall profit.

  * Copiers were the most profitable sub-category, while Tables and Bookcases incurred losses.

  * The Consumer segment generated the highest sales and profit.