E-Commerce-Dataset-Analysis-Using-MySQL-and-Python

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Introduction

This project demonstrates the analysis of an e-commerce dataset using both MySQL for querying data and Python for further insights and visualizations. The dataset includes multiple tables such as customers, orders, products, and payments. The objective is to extract meaningful insights, like customer distribution, order trends, and sales performance, while utilizing both SQL for database operations and Python for data manipulation and visualization.

Objective 🎯

• Goal: To practice delivering actionable insights for the E-commerce Management Team by analyzing a sample dataset, developing metrics, and visualizing data using MySQL and Python.

• Role: As a Data Analyst (for learning purposes), I practiced analyzing e-commerce data, calculating key metrics, and creating a dashboard based on a mock-up, simulating real-world scenarios.

• Tasks:

  • Develop and calculate key metrics using MySQL and Python.
  • Explore additional insights beyond the mock-up and metric list to enhance my skills in data analysis and visualization.

Metrics Created

• Total Sales: Sum of payment values across all orders.
• Average Order Value (AOV): Total sales divided by the number of orders.
• Total Number of Orders: Total count of orders placed in the dataset.
• Orders by Month: Total number of orders placed each month.
• Top 5 Product Categories by Sales: The five product categories with the highest total sales.
• Customer Retention Rate: Percentage of customers who placed more than one order.
• Percentage of Orders Paid in Installments: Percentage of total orders where the payment method was in installments.
• Top 5 States by Customer Orders: The five states with the highest number of orders.
• Sales Growth Rate: Percentage change in total sales from one period (e.g., month, year) to the next.
• Most Common Payment Type: The most frequently used payment method.

Dataset 📀

The project utilized data from the following five CSV files:

• orders Contains information about customer orders.
• customers: Contains details about customers.
• order_items: Contains data on the individual items within each order.
• payments: Contains information about the payments made for orders.
• products: Contains details of the products sold in the e-commerce store.
• sellers: Contains information about sellers.

Technology 💻

• MySQL: For querying and analyzing the e-commerce dataset, extracting key metrics, and joining multiple tables.
• Python (Pandas, NumPy, Matplotlib, Seaborn): For data processing, analysis, and visualizations.

Tools 🛠

• MySQL Workbench: To write SQL queries for extracting and analyzing data from the e-commerce database.
• Jupyter Notebook: For running Python code, data manipulation, and creating visualizations.

Conclusion 💡

This project provided hands-on experience in analyzing e-commerce data using MySQL and Python, from calculating key metrics. Through this practice, I gained deeper insights into customer behavior, sales trends, and product performance. Leveraging these tools and technologies not only enhanced my technical skills but also prepared me to deliver actionable insights in a real-world e-commerce environment.