Walmart-Sales-Analysis-using-SQL

🛠️ Tools and Libraries

• MYSQL Workbench for preprocessing and data analysis
• Generative AI/ CHAT GPT for Data Visualisation and Summary

1. Summary of Key Insights

Database & Table Creation

• The script creates a database (WalmartSales) and a sales table containing attributes like invoice_id, branch, city, customer_type, gender, product_line, unit_price, quantity, tax_pct, total, date, time, payment, cogs, gross_margin_pct, gross_income, and rating.

2. Basic Data Exploration

• Queries retrieve all sales records (SELECT * FROM sales;).
• The script identifies the earliest (MIN(time)) and latest (MAX(time)) sales transactions, confirming operating hours from 10 AM to 9 PM.

3. Feature Engineering

• Time-based features are extracted:
• Sales transactions are categorized into Morning, Afternoon, and Evening periods.
• A day_type column is created to classify sales as Weekday or Weekend.
• Customer behavior analysis:
• Sales are grouped by Customer Type, Payment Method, and Gender.

4. Sales Performance Analysis

• Branch-wise Sales Performance: Summarizes total sales per branch.
• Most and Least Popular Product Lines: Identifies the best- and worst-selling product categories.
• Revenue Trends Over Time: Analyzes how revenue varies by day, week, and month.

5. Customer Insights

• Gender-based purchasing patterns.
• Customer rating trends to analyze service satisfaction.

6. Visualization of sales analysis

The bar chart illustrates sales activity across store operating hours (10 AM to 9 PM). The highest sales volume is observed in the evening hours (5 PM - 7 PM), likely due to peak shopping times.

7. Here are the visualizations for further analysis:

1. Sales by Day Type (Weekday vs. Weekend)

• Sales by Day Type (Weekday vs. Weekend) – More sales occur on weekdays than weekends.

2. Sales by Customer Type

• Sales by Customer Type – Sales are evenly split between Members and Normal customers.

3. Sales by Product Line

• Sales by Product Line – Groceries have the highest sales, followed by Clothing and Electronics.

4. Revenue Trends Over Time

• Revenue Trends Over Time – Fluctuations in revenue suggest periodic spikes, possibly due to promotions or seasonal demand.