E-Commerce Data Analysis Report

Description

This repository contains an in-depth analysis of e-commerce data using SQL and Python. The project focuses on extracting valuable insights from the dataset and includes a combination of data manipulation, querying, and visualization techniques.

Features

• Data Loading and Cleaning: Prepares raw e-commerce data for analysis.
• SQL Queries: Efficient data retrieval and manipulation using SQL.
• Python Integration: Advanced analysis and visualization using Python libraries.
• Key Insights: Focus on sales trends, customer behavior, and product performance.

Tools and Technologies

• Jupyter Notebook: For organizing and running the analysis.
• Python: Utilized for data processing and visualization.
• SQL: For querying and manipulating data efficiently.
• Libraries: pandas, matplotlib, seaborn, sqlite3 (or another SQL interface).

Usage

1. Clone the repository:

   git clone https://github.com/Mohammad-Ali-SK/SQL-Python_E_commerce_Data-Analysis.git

2. Navigate to the project directory:

   cd ecommerce-report

3. Open the Jupyter Notebook:

   jupyter notebook SQL+python.ipynb

Dataset

The analysis is performed on a sample e-commerce dataset containing information such as:

• Orders
• Customers
• Products
• Sales performance

Note: Ensure the dataset is available in the expected directory structure for the notebook to function correctly.

Results

Key takeaways from the analysis include:

• Insights into customer purchasing behavior.
• Identification of top-performing products.
• Revenue trends across different periods.