${\color{red} Sales\ Analyis\ using\ MySQL}$

✅ ${\color{green} Objective}$

• Analyze company sales data to uncover performance trends, customer behavior, and product-level insights using SQL queries.

🛠️ ${\color{grey} Tools\ Used}$

• MySQL (or compatible RDBMS)

• SQL (Structured Query Language)

🔄 ${\color{blue} Steps\ Included}$

1. Data exploration using SELECT * and filtering conditions.

2. Aggregation of sales data by city, product, and customer.

3. Pattern matching using LIKE for customer and product searches.

4. Ranking and limiting results with ORDER BY, LIMIT, and RANK() functions.

5. Use of analytical queries for detailed insights.

📊 ${\color{orange} Analysis\ Included}$

• Top profitable products and high-value orders.

• Sales performance by shipping mode and region.

• Revenue, average unit cost, and order volume metrics.

• Customer and product-based order frequency.

• City-level sales ranking.

🔍 ${\color{brown} Key\ Insights}$

• Technology sales in Ireland after 2020 show specific trends.

• Certain cities dominate in total sales volume.

• A few customers and products significantly drive revenue.

• Economy shipping correlates with high-value transactions.

🧾 ${\color{black} Conclusion}$

• SQL is a powerful tool for extracting actionable business insights from raw sales data. This project demonstrates practical data analysis techniques to support decision-making and strategic planning.