This Jupyter notebook contains an analysis of Amazon sales data. The analysis explores various aspects of sales performance including product categories, customer demographics, sales trends, and geographical distribution of customers.
The dataset includes information about:
- Products sold on Amazon
- Customer demographics and locations
- Sales figures and metrics
- Order dates and fulfillment details
- Initial data loading and inspection
- Handling missing values
- Data type conversions
- Basic statistical summaries
- Analysis of top-selling products
- Sales distribution across different categories
- Revenue analysis
- Customer location distribution
- Relationship between customer demographics and purchasing behavior
- Customer segments based on purchase history
The notebook includes various visualizations:
- Bar charts showing product sales by category
- Geographic visualizations of customer locations
- Time series analysis of sales trends
- Correlation plots between different sales metrics
- Identified top-performing product categories
- Discovered geographical patterns in customer distribution
- Analyzed relationships between customer location and product preferences
- Determined sales trends over time
- Python
- Pandas for data manipulation
- Matplotlib and other visualization libraries for charts and graphs
- NumPy for numerical operations
- Ensure you have Jupyter Notebook or JupyterLab installed
- Install required dependencies: pandas, matplotlib, numpy
- Open the notebook in Jupyter
- Run the cells sequentially to reproduce the analysis
- Implement predictive models for sales forecasting
This analysis provides valuable insights into Amazon sales patterns, helping to identify successful products, target customer segments, and optimize marketing strategies based on geographical and demographic factors.