🛒 SQL for Data Analysis on Retail Sales

📌 Overview

This project is part of a Data Analyst Internship Task (Task 4 – SQL for Data Analysis).
The objective is to use SQL queries to clean, explore, and analyze an E-commerce retail sales dataset.

Repository: harshharsha17/SQL-for-Data-Analysis

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📂 Files in Repository

• Retail_sales_analysis.csv → Dataset used for analysis.
• task_4_sql_data_analysis_retail_sales.sql → SQL script containing all queries.
• README.md → Documentation explaining objectives, steps, and outcomes.
• Query screenshots (Query1.png … Query10.png) showing output of SQL queries.

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🛠 Tools Used

• Database: PostgreSQL / MySQL / SQLite
• Queries: SQL (DML + DDL)

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🔍 Steps Performed

1. Database & Table Creation

• Created database: task_4
• Created table: retail_sales with columns:
  transactions_id, sale_date, sale_time, customer_id, gender, age, category, quantity, price_per_unit, cogs, total_sale.

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2. Data Cleaning

• Checked for NULL values in critical fields.
• Deleted rows with missing values to ensure accuracy in analysis.

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3. Data Exploration

• Counted total number of sales.
• Found unique customers.
• Extracted distinct product categories (Electronics, Clothing, Beauty).

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4. Data Analysis Queries

✅ Key SQL Queries answered:

1. Sales made on a specific date (2022-11-05).
2. Clothing sales with quantity > 4 in November 2022.
3. Total sales & order count per category.
4. Average age of Beauty product customers.
5. Transactions with sales > 1000.
6. Transactions by gender per category.
7. Best selling month (using RANK & window functions).
8. Top 5 customers based on sales.
9. Unique customers per category.
10. Orders distribution by shift (Morning, Afternoon, Evening).

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📖 Concepts Covered

• SQL Basics: SELECT, WHERE, ORDER BY, GROUP BY
• Aggregate Functions: SUM, AVG, COUNT, ROUND
• DISTINCT keyword
• CASE statements for time-based analysis
• Window Functions (RANK)
• Subqueries
• Query Optimization (using indexes)
• Views for reusable queries

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📸 Query Outputs

Q1: Sales on 2022-11-05

Q2: Clothing category with quantity > 4 in Nov-2022

Q3: Total sales per category

Q4: Average age of Beauty category customers

Q5: Transactions with total_sale > 1000

Q6: Transactions by gender per category

Q7: Best selling month per year

Q8: Top 5 customers based on total sales

Q9: Unique customers per category

Q10: Orders distribution by shift (Morning, Afternoon, Evening)

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🎯 Outcome

✔ Learned to clean and manipulate SQL datasets.
✔ Performed exploratory and analytical queries.
✔ Gained insights into customer behavior, sales trends, and category performance.

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🚀 How to Run

1. Clone this repository:

   git clone https://github.com/harshharsha17/SQL-for-Data-Analysis.git