https://github.com/madhuresh2011/amazon-sales-report-analysis-project-using-python

This project focuses on analyzing Amazon sales data using Python to uncover insights into sales performance, customer behavior, and product trends
https://github.com/madhuresh2011/amazon-sales-report-analysis-project-using-python

charts cleaning-data data-analysis jupyter-notebook matplotlib numpy pandas python seaborn visualization

Last synced: 18 days ago
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This project focuses on analyzing Amazon sales data using Python to uncover insights into sales performance, customer behavior, and product trends

• Host: GitHub
• URL: https://github.com/madhuresh2011/amazon-sales-report-analysis-project-using-python
• Owner: Madhuresh2011
• Created: 2024-11-22T17:55:30.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-26T17:55:38.000Z (21 days ago)
• Last Synced: 2024-12-26T18:42:03.901Z (21 days ago)
• Topics: charts, cleaning-data, data-analysis, jupyter-notebook, matplotlib, numpy, pandas, python, seaborn, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 4.23 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Amazon-Sales-Report-Analysis

### Description:

This project focuses on analyzing Amazon sales data using Python to uncover insights into sales performance, customer behavior, and product trends. The project utilizes Python's robust data manipulation and visualization libraries to process large datasets, generate detailed reports, and provide actionable insights for decision-making.

### Key Features:

- Amazon sales data, including order details, customer information, and product categories.

- Python libraries like pandas were used to clean the data, handle missing values, and ensure consistency.

### Data Analysis:

- Performed exploratory data analysis (EDA) to identify patterns and trends in sales.

- Analyzed metrics such as total sales, profit margins, order volumes, and customer demographics.

- Top-performing products.

- Seasonal sales trends.

- Customer lifetime value (CLV).

### Data Visualization:

- Leveraged Python libraries like matplotlib and seaborn to create insightful visualizations, including:

- Bar charts for product category sales.

- Line graphs for monthly or quarterly sales trends.

- Heatmaps for regional sales distribution.

- Pie charts for payment method analysis.

### Conclusion:

This project highlights the power of Python in analyzing and visualizing Amazon sales data. By providing actionable insights, it enables businesses to identify opportunities, optimize strategies, and improve overall sales performance. The solution is versatile and can be adapted for other e-commerce platforms as well.