Key-Findings

E-Commerce Sales Data Analysis: Uncovering Customer Trends and Product Insights
This analysis delves into a dataset containing hundreds of thousands of electronics store purchases. By leveraging Python libraries like pandas, NumPy, and matplotlib, we extracted valuable insights to aid business decisions.

Key Findings:

Seasonal Sales Trends: December emerged as the month with the highest sales, indicating potential for targeted marketing campaigns during the holiday season.
Geographic Analysis: New York, Los Angeles, and San Francisco were identified as the top cities with the most orders, highlighting the importance of tailoring strategies to these key markets.
Product Performance: "AAA Batteries" topped the charts for most-sold products. Interestingly, a correlation was observed between product price and order quantity, suggesting a preference for cheaper items.
Product Bundling: By analyzing frequently co-purchased products, we gained valuable insights into customer buying habits. This information can be used to develop effective product recommendation systems.
Top Product Trends: We analyzed sales trends for the top 5 most-sold products, revealing a surge in purchases during October, November, and December. This suggests potential seasonal demand fluctuations.
Overall, this analysis provides a comprehensive understanding of customer behavior and product performance within the electronics store. The insights can be used to optimize marketing strategies, product offerings, and overall business operations.