SQL-Python E-commerce Analysis
This project demonstrates how to analyze an E-commerce dataset using Python (Pandas, Matplotlib, Seaborn) and MySQL for querying and managing data. The project focuses on extracting insights from customers, orders, payments, sellers, products, and geolocation datasets.
📌 Project Overview Loaded CSV files into MySQL database tables. Performed SQL queries for data analysis. Connected MySQL with Python using mysql-connector. Cleaned, transformed, and explored datasets using Pandas. Created visualizations for better insights.
Created visualizations for better insights.
📂 Dataset The project uses E-commerce datasets (CSV files): customers.csv orders.csv sellers.csv products.csv geolocation.csv payments.csv order_items.csv
⚙️ Technologies Used Python 🐍 (Pandas, Matplotlib, Seaborn, MySQL Connector) MySQL (Data storage and queries) Jupyter Notebook (Development environment) 📊 Key Insights
Order distribution by months and years Customer and seller analysis Payment method trends Product performance Geographical patterns 🚀 How to Run the Project
-
Clone this repository: git clone https://github.com/your-username/SQL-python-Ecom.git cd SQL-python-Ecom
-
Install required libraries: pip install pandas mysql-connector-python matplotlib seaborn
-
Import the dataset into MySQL (update credentials in the notebook).
-
Open the Jupyter Notebook: jupyter notebook SQL-python-Ecom.ipynb
⚙️ Technologies Used