E-Commerce Data Analysis Project

Project Overview

E-Commerce Data Analysis Project is a data engineering project aimed at analyzing large-scale E-Commerce data to generate actionable insights. This project helps the marketing department identify key sales trends, product popularity, and regional performance, supporting data-driven business decisions.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Installation
• Usage
• Project Structure
• Insights and Visualizations
• Future Enhancements

Features

• Data Generation: Synthetic e-commerce data with realistic variations across products, locations, and time periods.
• Data Analysis: Analyze sales performance by category, location, and product.
• Visualization: Generate insightful visualizations to track trends, product popularity, and regional sales performance.
• Scalable: Easily customizable for larger datasets or additional features.

Technologies Used

• Programming Language: Python
• Libraries:
  • pandas (Data manipulation)
  • matplotlib (Data visualization)
  • csv (Data generation)

Installation

1. Clone the repository:

   git clone https://github.com/manojkandi/E_Commerce_Data_Analysis.git
   cd E_Commerce_Data_Analysis

2. Create and activate a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # For Linux/macOS
   env\Scripts\activate     # For Windows

3. Install dependencies:

   pip install pandas matplotlib

Usage

Step 1: Generate Synthetic E-Commerce Data

Run the data generator to create a dataset with 10,000 records:

python ecommerce_data_generator.py

Step 2: Analyze and Visualize Data

Execute the analysis script to generate visual insights:

python ecommerce_analysis.py

Step 3: View Visualizations

The script will generate the following plots:

• Monthly Sales Trends
• Top 10 Most Popular Products
• Sales by Location
• Sales Performance by Category

Project Structure

E-Commerce Data Analysis Project/
│
├── ecommerce_data_generator.py       # Script to generate synthetic CSV data
├── ecommerce_analysis.py             # Script to analyze and visualize the data
├── ecommerce_data.csv                # Generated dataset (after running the generator script)
└── README.md                         # Project documentation

Insights and Visualizations

1. Monthly Sales Trends

Tracks total sales per month, helping identify seasonal trends.

2. Top 10 Most Popular Products

Displays the most frequently purchased products by quantity.

3. Sales by Location

Highlights the total sales in different geographic regions.

4. Sales Performance by Category

Breaks down total sales by product category.

Future Enhancements

• Interactive Dashboards: Integrate with Tableau or Power BI for dynamic visualizations.
• SQL Integration: Store and query data from a relational database for better scalability.
• Predictive Analytics: Implement machine learning models to forecast future sales trends.