📊 E-Commerce SQL Exploratory Data Analysis (Brazil Region)

📝 Project Overview

This project performs Exploratory Data Analysis (EDA) on a Brazilian e-commerce dataset using SQL queries.
The dataset consists of multiple interconnected tables (customers, orders, payments, products, sellers, etc.), and the objective is to uncover business insights and customer behavior patterns.

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📂 Dataset Description

The project uses tables from the SQL_Target database:

• customers – customer demographics (city, state, ID mapping).
• geolocation – geographical details (lat/long, cities, states).
• order_items – details about purchased products, prices, freight values.
• order_reviews – customer reviews and ratings.
• orders_1 – orders placed, timestamps, delivery details.
• payments – payment methods and amounts.
• products – product catalog with attributes.
• sellers – seller details and locations.

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🔍 Analysis Performed

Key insights generated through SQL queries include:

1. Data Profiling

   • Data types of all columns in the customers table.
   • Order purchase time range.

2. Customer Insights

   • Cities and states of customers ordering within specific periods.
   • Customer distribution across Brazilian states.

3. Order Trends

   • Yearly and monthly order volume.
   • Growth trend of e-commerce orders.
   • Orders placed by time of day (dawn, morning, afternoon, night).

4. Payments & Revenue

   • % increase in order cost (2017 vs. 2018).
   • Orders by payment type (monthly breakdown).
   • Orders by number of installments.

5. Delivery & Logistics

   • Average delivery time across states.
   • Top 5 states with highest & lowest freight values.
   • Gap between actual vs. estimated delivery times.

6. Price & Freight Analysis

   • Mean and total values of product prices and freight charges by customer state.

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⚙️ Tech Stack

• SQL (Google BigQuery / any SQL engine)
• Dataset: Brazilian e-commerce data
• Environment: Jupyter Notebook, BigQuery console, or other SQL IDE