{"id":23201138,"url":"https://github.com/omkarb40/e-commerce-data-analysis","last_synced_at":"2026-01-18T00:21:57.701Z","repository":{"id":262328381,"uuid":"885498535","full_name":"omkarb40/E-commerce-Data-Analysis","owner":"omkarb40","description":"This project contains SQL scripts designed to perform a detailed Exploratory Data Analysis (EDA) on an e-commerce dataset. 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The goal is to extract actionable insights about customer behavior, product performance, sales trends, and operational efficiency, helping businesses make data-driven decisions.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Analysis Sections](#analysis-sections)\n  - [Overview Statistics](#overview-statistics)\n  - [Sales Analysis](#sales-analysis)\n  - [Customer Behavior Analysis](#customer-behavior-analysis)\n  - [Product Category Analysis](#product-category-analysis)\n  - [Customer Segmentation (RFM Analysis)](#customer-segmentation-rfm-analysis)\n  - [Order Processing Analysis](#order-processing-analysis)\n  - [Geographic Analysis](#geographic-analysis)\n  - [Time-based Analysis](#time-based-analysis)\n  - [Product Association Analysis](#product-association-analysis)\n- [Setup and Requirements](#setup-and-requirements)\n- [Usage](#usage)\n\n## Introduction\n\nThe SQL script performs various analyses on an e-commerce dataset stored in relational database tables. It addresses key business questions such as:\n- Who are the most valuable customers?\n- What are the top-selling products?\n- What trends are visible in sales and orders over time?\n- How efficient is the order processing pipeline?\n- What are the strongest product associations?\n\n## Analysis Sections\n\n### Overview Statistics\n\nBasic statistics providing a high-level overview of the dataset:\n- Total unique customers\n- Total unique products\n- Total unique orders\n- Average number of items per order\n\n### Sales Analysis\n\nInsights into revenue trends and product performance:\n- Monthly revenue trends\n- Top 10 best-selling products by revenue\n\n### Customer Behavior Analysis\n\nUnderstanding customer purchase patterns:\n- Number of orders, average order value, and purchase recency for each customer\n- Segmentation of customers into activity-based groups (e.g., Active, Churn Risk, Churned)\n\n### Product Category Analysis\n\nRevenue trends by product categories:\n- Monthly revenue by product category (requires category information in the dataset)\n- Count of unique products per category\n\n### Customer Segmentation (RFM Analysis)\n\nSegmentation of customers based on:\n- **Recency**: Time since the last purchase\n- **Frequency**: Total number of purchases\n- **Monetary**: Total spending\nCustomers are segmented into groups like VIP, Regular, and Occasional based on their RFM scores.\n\n### Order Processing Analysis\n\nAnalyzes order processing efficiency:\n- Distribution of processing times (e.g., 0-24 hours, 24-48 hours)\n- Average processing times (if shipping timestamps are available)\n\n### Geographic Analysis\n\nRegional sales performance:\n- Revenue and average order value by country or region\n- Identifying high-performing cities or regions\n\n### Time-based Analysis\n\nTrends based on time:\n- Sales patterns by day of the week\n- Sales patterns by hour of the day\n\n### Product Association Analysis\n\nFrequent product combinations:\n- Top product pairs frequently bought together\n- Association analysis for complementary products\n\n## Setup and Requirements\n\n### Prerequisites\n- A SQL-compatible database (e.g., MySQL, PostgreSQL)\n- Dataset tables:\n  - `e_commerce_cleaned` or equivalent: Includes columns such as `InvoiceNo`, `StockCode`, `Description`, `Quantity`, `InvoiceDate`, `UnitPrice`, `CustomerID`, and `Country`.\n  - Additional category or shipping information is optional but can enrich analysis.\n\n### Data Preparation\nEnsure the dataset is imported into your database. The script assumes the following key columns:\n- **Order Details**: `InvoiceNo`, `InvoiceDate`\n- **Product Details**: `Description`, `StockCode`, `Quantity`, `UnitPrice`\n- **Customer Details**: `CustomerID`, `Country`\n\n## Usage\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/omkarb40/E-commerce-Data-Analysis.git\n   cd ecommerce-sql-eda\n2. Load your dataset into a SQL database.\n3. Run the SQL script:\n    SOURCE ecommerce_eda.sql;\n4. Review the query results to explore insights.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomkarb40%2Fe-commerce-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomkarb40%2Fe-commerce-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomkarb40%2Fe-commerce-data-analysis/lists"}