Ecommerce---SQL-Data-Analytics-Project

Project Overview

This project explores various aspects of ecommerce business performance using SQL and Python. It includes:

• Customer Behavior Analysis: Segmenting customers, investigating payment methods, and analyzing order patterns.
• Sales Trend Analysis: Studying sales performance, including total sales and product popularity.
• Order Analytics: Analyzing the total orders, revenues, shipping times, and more.

Dataset

You can find all tables via kaggle !link(https://www.kaggle.com/code/vigneshwaranchokka/ecommerce-data-analytics-project).

Data Description

The dataset used for analysis includes the following tables:

• Orders: Order details, including order IDs, customer IDs, and order timestamps.
• Order Items: Information about the products within each order.
• Payments: Payment methods and amounts for each order.
• Products: Details of products, including product categories and prices.
• Reviews: Customer feedback and review scores for products.
• Sellers: Information about sellers, including seller IDs and locations.

Data visualizations are created using matplotlib and seaborn to present the findings clearly.

Requirements

To run this project, you will need:

• Python 3.x
• pandas
• matplotlib
• seaborn
• sqlite3 (or any other database connection module depending on the database you're using)
• Jupyter Notebooks (optional for interactive use)

You can install the required Python libraries using pip:

pip install pandas matplotlib seaborn sqlite3


Acknowledgments
Thanks to the authors of the dataset for making it available.
Special thanks to the Python and SQL communities for their open-source libraries and resources.