Amazon SQL Analysis

This project explores Amazon product and user data using SQL, with the goal of understanding product categories, user purchasing behavior, and discounts.

Project Overview

The analysis focuses on:

• How products are distributed across categories
• How actively users purchase items
• How much users save through discounts
• Differences between declared and real discounts
• How stable or variable discounts are across categories

Dataset Assumptions

The analysis is based on a table named amazon with the following fields:

• category
• user_id
• user_name
• actual_price
• discounted_price
• discount_percentage

Prices are stored as strings (e.g. ₹1,299) and are cleaned during the analysis.

Analysis Steps

1. Category Analysis

   • Count of products per category

2. User Activity

   • Number of purchased items per user

3. User Savings Segmentation

   • Total spending vs discounted spending
   • Classification of users based on savings behavior

4. Discount Analysis

   • Comparison between declared and real discounts per category

5. Variance Analysis

   • Discount rate variability across categories
   • Savings amount variability across categories

Technical Notes

• SQL is used for all data processing and analysis.
• A database view is created to clean and convert price fields into numeric values.
• Variance is calculated using the formula: VAR(x)=AVG(x^2)-(AVG(x))^2

Purpose

This project was created to practice SQL analytics, data cleaning, and basic business-oriented analysis on real-world styled e-commerce data.